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DOI: 10.1039/C7SC04531J
(Edge Article)
Chem. Sci., 2018, Advance Article

Louis Lagardère^{abc},
Luc-Henri Jolly^{b},
Filippo Lipparini^{d},
Félix Aviat^{c},
Benjamin Stamm^{e},
Zhifeng F. Jing^{f},
Matthew Harger^{f},
Hedieh Torabifard^{g},
G. Andrés Cisneros^{h},
Michael J. Schnieders^{i},
Nohad Gresh^{c},
Yvon Maday^{jkl},
Pengyu Y. Ren^{f},
Jay W. Ponder^{m} and
Jean-Philip Piquemal*^{cfk}
^{a}Sorbonne Université, Institut des Sciences du Calcul et des Données, Paris, France
^{b}Sorbonne Université, Institut Parisien de Chimie Physique et Théorique, CNRS, FR 2622, Paris, France
^{c}Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616, CNRS, Paris, France. E-mail: jpp@lct.jussieu.fr
^{d}Universita di Pisa, Dipartimento di Chimica e Chimica Industriale, Pisa, Italy
^{e}MATHCCES, Department of Mathematics, RWTH Aachen University, Aachen, Germany
^{f}The University of Texas at Austin, Department of Biomedical Engineering, TX, USA
^{g}Department of Chemistry, Wayne State University, Detroit, MI 48202, USA
^{h}Department of Chemistry, University of North Texas, Denton, TX 76202, USA
^{i}The University of Iowa, Department of Biomedical Engineering, Iowa City, IA, USA
^{j}Sorbonne Université, Laboratoire Jacques-Louis Lions, UMR 7598, CNRS, Paris, France
^{k}Institut Universitaire de France, Paris, France
^{l}Brown University, Division of Applied Maths, Providence, RI, USA
^{m}Washington University in Saint Louis, Department of Chemistry, Saint Louis, MI, USA

Received
18th October 2017
, Accepted 24th November 2017

First published on 27th November 2017

We present Tinker-HP, a massively MPI parallel package dedicated to classical molecular dynamics (MD) and to multiscale simulations, using advanced polarizable force fields (PFF) encompassing distributed multipoles electrostatics. Tinker-HP is an evolution of the popular Tinker package code that conserves its simplicity of use and its reference double precision implementation for CPUs. Grounded on interdisciplinary efforts with applied mathematics, Tinker-HP allows for long polarizable MD simulations on large systems up to millions of atoms. We detail in the paper the newly developed extension of massively parallel 3D spatial decomposition to point dipole polarizable models as well as their coupling to efficient Krylov iterative and non-iterative polarization solvers. The design of the code allows the use of various computer systems ranging from laboratory workstations to modern petascale supercomputers with thousands of cores. Tinker-HP proposes therefore the first high-performance scalable CPU computing environment for the development of next generation point dipole PFFs and for production simulations. Strategies linking Tinker-HP to Quantum Mechanics (QM) in the framework of multiscale polarizable self-consistent QM/MD simulations are also provided. The possibilities, performances and scalability of the software are demonstrated via benchmarks calculations using the polarizable AMOEBA force field on systems ranging from large water boxes of increasing size and ionic liquids to (very) large biosystems encompassing several proteins as well as the complete satellite tobacco mosaic virus and ribosome structures. For small systems, Tinker-HP appears to be competitive with the Tinker-OpenMM GPU implementation of Tinker. As the system size grows, Tinker-HP remains operational thanks to its access to distributed memory and takes advantage of its new algorithmic enabling for stable long timescale polarizable simulations. Overall, a several thousand-fold acceleration over a single-core computation is observed for the largest systems. The extension of the present CPU implementation of Tinker-HP to other computational platforms is discussed.

The purpose of the present work is to push the scalability improvements of Tinker through new algorithms to explore strategies enabling a 1000-fold and more speedup. These new developments aim towards modern “big Iron” petascale supercomputers using distributed memory and the code design also offers consequent speedups on laboratory clusters and on multicore desktop stations. The philosophy here is to build a highly scalable double precision code, fully compatible and consistent with the canonical reference Tinker and Tinker-OpenMM codes. As the new code remains a part of the Tinker package, it is designed to keep its user-friendliness offered to both developers and users but also to provide an extended access to larger scale/longer timescale MD simulations on any type of CPU platforms. The incentive to produce such a reference double precision code is guided by the will to also perform scalable hybrid QM/MM MD simulations where rounding errors must be eliminated. This will bring us not to cut any corners in our numerical implementation with the key mantra that one should not scale at any cost, as the algorithms developed in this interdisciplinary project should be based on solid mathematical grounds.

The paper is organized as follows. First, we will present the newly developed extension of 3D spatial decomposition and memory distribution to polarizable point dipole models that is at the heart of Tinker-HP for short-range interactions. Then we will detail the handling of long-range electrostatic and polarization interactions with a new framework coupling Smooth Particle Ewald to Krylov iterative and non iterative polarization solvers. We will then introduce the possibilities of the software and show benchmarks for selected applications in the context of the AMOEBA PFF.^{24,36} Finally, we will present functionalities of Tinker-HP that go beyond MD simulations in periodic boundary conditions as we conclude by drawing some perspectives about evolutions of the code towards next HPC platforms.

Let us give more details about the algorithm and the main steps required to perform a time step of MD using this method. We assume that the simulated system resides in a box that has been divided in as many 3D blocks as the number of processors used. Let us focus on a processor that has been assigned a 3D block and let us assume that this processor knows the current positions, velocities and accelerations of the atoms currently belonging to this block. In integrator schemes such as velocity Verlet, the first integration step consists of an update of the local positions and a first update of the velocities. Because of these position changes, some atoms may cross the local block boundaries and need to be reassigned to neighboring blocks. This step, that we will call “reassign step” only requires local communications between a small number of neighboring processes.

In the second step, the forces are computed and used for the second update of the velocities. This requires the processor to know the positions of all atoms within the interaction cutoff, that have to be communicated from the processors assigned to the blocks that are at distance inferior or equal to the cutoff. We will call this step, which also involves local communications (but that may involve more distant processors than the previous one) “position comm” step. Once this is done, the algorithm loops back to the first step.

The communication volume involved in the position comm step can be reduced by taking into account the pairwise nature of the fundamental operations needed to compute the forces. Given a pair of atoms, in fact, one needs to choose which processor will perform the elementary force computation. This can be done on the basis of a geometrical argument. Among the various methods, that are also called neutral territory approaches,^{38} we choose the one presented by Shaw et al.,^{16} known as the midpoint method.^{38} This method picks out the processor that computes an interaction between two atoms as the one assigned to the subdomain where the center of the segment between the two atoms lies. As a consequence, each processor only needs to import information about atoms located at less than from its block: one can show that the communication volume is then, with d being the size of an edge of a subdomain, as represented schematically in Fig. 2. This is a significant reduction with respect to the naive method,^{38} especially at a high processors count. Note, however, that within this scheme, a processor might need to compute the elementary interaction between atoms that do not belong to its block.

Fig. 2 Illustration of the midpoint rule in 2D: the square of edge d represents a subdomain assigned to a process and the blue line delimits the area that has to be imported by the latter. |

Furthermore, once the elementary pairwise computation has been done, we can take advantage of Newton's third law and communicate the force back to both processors from which the positions originated (“force comm” step). This additional communication cost is in general negligible compared to the computational gain represented by the reduction of the computations of the forces by half.

Additionally, the midpoint approach is simple enough not to complicate too much the implementation, which is ideal for a platform like Tinker-HP, meant to be shared with a community of developers. Nevertheless, more elaborate techniques are interesting and have been shown to reduce asymptotically the amount of data that need to be communicated in the “position comm” step and in the “forces comm” step. We are currently studying these methods in the context of PFF to compare them to the present midpoint approach. Some of them should appear in future releases of our code.

The algorithmic structure of a parallel (short-range) MD step with spatial decomposition is shown in Fig. 3.

To address load balancing issues that may appear in non-homogeneous systems (when equally sized subdomains contain a very different number of atoms), a procedure in which the size of the subdomains is iteratively changed has been implemented.

Unfortunately, some data structures such as the arrays containing the global parameters are allocated once and for all and cannot be distributed. This is especially problematic for PFFs such as AMOEBA, that require more parameters than the classical ones: replicating these arrays for each processor would be prohibitive. This issue can be circumvented by using shared memory segments that can be managed with MPI (3.X) directives. This means that these data are allocated only once per node and are accessible by every processor within the node, reducing thus memory requirements by the number of processors of the node.

The computation of the polarization energy in PFFs using the induced dipole formulation consists of two steps. First, a set of 3N (N being the number of polarizable sites) induced dipoles has to be computed by minimizing the functional

Tμ = E, | (1) |

The computation of the induced dipoles by iterative methods represents not only an important additional computational cost, but also an important communication cost, as at each iteration two of the three communication steps described in Section 2 are required.

An essential part of our parallelization strategy is masking communication by computation in the parallel solvers whenever possible. This is achieved by using non-blocking MPI routines and by starting the receptions and the sendings of data as soon as possible, and, at the same time, verifying that the communications are finished as late as possible in the code, so that computations are being made between these two states. A schematic representation of a typical iteration of a polarization solver is shown in Fig. 4.

As we stated before, the method that we adopt for PBC is the Smooth Particle-Mesh Ewald^{47} (SPME). It has become a standard algorithm in modern biomolecular simulations to compute electrostatic interactions in periodic boundary conditions, thanks to its advantageous scaling. The method has been extended to PFFs^{48} as well as to multipolar interactions,^{49} possibly including penetration effects.^{50}

Let us explain the steps that are followed during a SPME computation for the electrostatic potential produced by distributed multipoles. The exact same considerations apply to the computation of the electrostatic and polarization forces and during a MVP computation during the iterative solution of the polarization equations. The electrostatic interactions are divided into two parts, one of which is short-range and is treated in the direct space, while the other is long-range and is treated in Fourier space. For the first, short-range part, the consideration made in Section 2 apply: we focus here on the reciprocal space computation. Such a computation requires the definition of a 3D grid and the use of Fast Fourier Transforms, which requires a significantly different parallelization strategy. The most standard one uses a 1D or 2D decomposition of the 3D grid and has been described elsewhere^{12,40} in detail. Let us summarize its main steps and analyze the parallelization strategy employed in Tinker-HP.

The SPME computation requires to distribute the multipoles on the grid using a B-spline interpolation and then to solve Poisson's equation in the reciprocal space. The distribution of the 3D grid is therefore what drives the parallelization strategy. In Tinker-HP, the grid is decomposed into 2D pencils, and each pencil is assigned to a processor. The first step of SPME consists into assigning charges or higher order multipoles to the gridpoints. As explained in our previous work,^{40} this operation requires local communications between processors assigned to neighboring portions of the grid.

The second step consist into switching to Fourier space by applying a forward FFT to the grid that has just been filled. In Tinker-HP, this is entirely handled by the 2DECOMP&FFT library.^{51,52}

Then, the convolution with a pair potential^{47} is done in Fourier space, which is a simple multiplication that is naturally distributed among the processors without any necessary communication.

Finally, the result of this multiplication is transformed back to real space by applying a backward FFT, which is also taken care of by 2DECOMP&FFT in Tinker-HP.

A final local set of communications between processors responsible for neighboring portions of the grid is done, followed by local multiplication with B-splines. A schematic representation of these steps is shown in Fig. 5.

Fig. 5 Schematic representation of the computation of the reciprocal part of the electrostatic energy and forces with SPME. |

Naturally, because the Fourier space decomposition of the grid may not fit exactly the 3D spatial decomposition, additional communications of positions are required before starting the reciprocal part of a SPME computation. Furthermore, when electrostatic or polarization forces are computed in this way, or after a matrix-vector multiplication in an iteration of a polarization solver, communication of some of these forces or dipoles are required.

Lagardère et al. showed^{40} that the reciprocal part of SPME presented just above does not scale as well as the direct part with the number of processors, because of the relatively poor parallel scaling of the FFTs. Furthermore, because reciprocal space and direct space computations are independent and because reciprocal space is usually computationally cheaper, a usual strategy is to assign a smaller group of processors to reciprocal space and the rest to the direct space. This strategy can be used in Tinker-HP for both permanent electrostatics and polarization.

In that case, a difficulty arises in PFF computations. The load balancing between direct and reciprocal space computations is in fact essential to achieve a good scalability. However, the relative cost of direct and reciprocal computations is different for permanent electrostatics and MVP required for the computation of the induced dipoles. At this moment, only heuristic strategies have been implemented in Tinker-HP to handle this problem.

List builder. As we stated in the method section, Tinker-HP is designed to be used on all types of CPU-based computer systems ranging from desktop computer to supercomputers. To do so, the package embodies a fast O(N) massively parallel list builder that is designed for both an extensive use of a large number of cores and to enable also an efficient treatment on a small number of cores.

Polarization solvers. Massively parallel implementation of various polarization Krylov solvers are present and includes iterative methods such as PCG, JI/DIIS. Both approaches can be used in connection with Kolafaś Alway Stable Predictor (ASPC)^{53} that reduces significantly the iteration numbers for 1 fs and 2 fs timesteps simulations (see ref. 43 for discussion). An efficient non-iterative/fixed cost approach is also available: the Truncated Conjugate Gradient (TCG). TCG is implemented at the TCG1 and TCG2 levels with various refinements.^{42,43} The TCG approaches are a strong asset of Tinker-HP as they accurately reproduce energy surfaces at a reduced computational cost and provide analytical forces. Such an approach avoids numerical drifts appearing with iterative methods and therefore brings enhanced energy conservation for long simulations. It is also fully time-reversible and compatible with the use of larger time-steps.

It is important to point out that an important choice in the Tinker-HP strategy is to keep accuracy to the maximum by retaining a double-precision approach. By definition, GPUs have the strong advantage of using mixed precision which has been shown to produce more stability than simple precision computations. The strategy here is to build on the availability of the double precision to use algorithms that should/could not be used in mixed precision but are expected to be fully operational and faster in our case. For example, any CG methods are sensitive to precision (the symmetry of matrices being lost) as is the case for predictor-correctors such as the ASPC. Tinker-HP offers a full use of these strategies and compensates for the extra computational cost of double precision by more dependable algorithms.

Integrators. Most of the integrators available in Tinker have been implemented including, namely velocity Verlet, Beeman and RESPA^{54} which allows production MD simulations with 2 fs time steps, and 3 fs timesteps using H mass repartitioning.

Simulation ensembles and associated tools. NVE, NVT and NPT simulations are possible. Bussi and Berendsen thermostats are available. NPT simulations are also implemented with a Berendsen barostat.

Restraints and soft cores van der Waals. Position, distance, angle, torsions and centroid based harmonic restraints as well as softcore van der Waals and scaled electrostatics for free energy calculations are available.

Geometry optimization. To prepare large systems encompassing millions of atoms through geometry optimization, Tinker-HP offers a massively parallel version of Tinker's limited memory BFGS quasi-newton nonlinear optimization routine (LBFGS).

Advanced point dipole polarizable force fields. Tinker-HP allows for electrostatics to range from point charges to fully distributed multipoles (up to quadrupoles), point dipole polarization approaches using distributed polarizabilities^{41} coupled to Thole (or dual Thole) damping approaches as well as van der Waals interactions using the Lennard-Jones or the Halgren functions. This choice was motivated as these functional forms have been extensively used by various research groups that could therefore easily use Tinker-HP with their own parametrizations. Presently, two polarizable force field models, both relying on the Thole/point dipole polarization model, are available. The first model is the AMBER f99 polarizable model. It is limited to point charges to compute the permanent electrostatics and uses a 6–12 Lennard Jones for the van der Waals.^{20,55} The second is the AMOEBA polarizable model which has been shown to have a wide applicability for systems ranging from liquids to metals ions, including heavy ones, in solution and to proteins and to DNA/RNA.^{24,36,37,56–58} A major difference compared to the AMBER model is the replacement of the fixed partial charge model with polarizable distributed atomic multipoles till quadrupoles moments, allowing accurate reproduction of molecular electrostatic potentials, and higher resolution rendering of difficult directional effects in hydrogen bonding and other interactions. van der Waals interactions are also different and use the Halgren buffered 14–7 function.^{59} The AMOEBA polarizable model responds to changing or heterogeneous molecular environments and its parameterization was performed against gas phase experimental data and high-level quantum mechanical results. The AMOEBA model includes high accuracy water model as well as parametrization for organic molecules, proteins,^{60} ions and DNA/RNA complexes.

Classical force fields. By construction, the software is able to perform classical force field simulations following the canonical Tinker initial implementation of the AMBER, CHARMM and OPLS potentials. Such force fields also benefit from the speed up of the massively parallel framework but our objective is to reach comparable performance to the AMBER and CHARMM (Domdec^{61}) CPU implementations. The detailed analysis of such code capabilities being beyond the scope of this paper, fully dedicated to polarizable models, and it will be discussed elsewhere. However, it can be noted that classical MM that requires much less work than PFFs allows for a 5–8 acceleration of the production per day over AMOEBA (depending on the use of TCG vs. PCG solvers) on the same computational platform, and will be used for hybrid simulations with PFFs coupled to non-polarizable parts of the system. For higher performances using Tinker, one could use the Tinker-OpenMM access to the OpenMM library implementation of such classical FF. For example, it is possible to produce 305 ns per day for DHFR with the same GTX 1080 card (mixed precision) and settings used in this work using the AMBER force field.

Water boxes. We first benchmarked the code on cubic water boxes of increasing size: from 96000 atoms up to 23.3 millions atoms. Table 1 summarizes the characteristics of these boxes: their size in Angstroms, the number of atoms they contain, the size of the associated PME grid and the name with which they will be referenced in the rest of the paper.

System | Puddle | Pond | Lake | Sea | Ocean |
---|---|---|---|---|---|

Number of atoms | 96000 | 288000 | 8640000 | 7776000 | 23328000 |

Size (of an edge) in Angstroms | 98.5 | 145 | 205.19 | 426.82 | 615.57 |

Size (of an edge) of the PME grid | 120 | 144 | 250 | 432 | 648 |

Fig. 6 show the detailed scalability up to almost 1 million atoms.

Fig. 6 Performance gain for the [dmim^{+}][cl^{−}] ionic liquid system (A) and the Puddle (B), Pond (C) and Lake (D) water boxes. |

A very good scalability is observed in the three cases. Table 3 displays the best production timings in ns per day. The code appears to be competitive with the GPU numbers extracted from Tinker-OpenMM even for a system such as the smallest waterbox test (Puddle, 96000 atoms). In this case, Tinker-HP is already 1.5 faster than a GTX 1080 card (3 times for a GTX 970) but with double precision compared to mixed precision arithmetics used by GPUS. As we will discuss later in the case of proteins, the newly introduced 3D domain decomposition algorithmic for polarizable FF becomes more beneficial when the size of the system grows and a first advantage of Tinker-HP is to be able to use the distributed memory system of the CPU platform. Also for such large systems numerical instabilities of the polarization solvers that result in energy drifts^{40–43} are a key error that must be contained. Double precision is highly preferable when one wants to use advanced conjugate gradient solvers (and Krylov approaches in general). Tinker-HP has an advantage as it affords mathematically robust solutions for “drift-free” polarization solvers (Truncated Conjugate Gradient, TCG^{42,43}) with analytic forces. Such techniques allow for (very) long simulations. A stable adaptation of these methods to mixed precision hardware (i.e. GPUs) is underway but is mathematically non-trivial. Note that for short to medium simulations of a few dozen ns, the discussion is without object as the drifting issue will remain negligeable offering a full applicability of GPUs acceleration. However, towards and beyond the microsecond, the analytical forces polarization solvers will be key for stable polarizable simulations. For the other benchmark cases, the speedup increases to a 5 and 6-fold over a GTX970 (2 and 3-fold over a GTX1080) for 288000 atoms (Pond) and 864000 atoms (Lake) water boxes respectively. For the Lake box, a detailed analysis of the scaling against ideal scaling is provided in ESI S2.† We then pushed the code towards its limits by testing very large systems including 7776000 and 23300000 atoms respectively. At these levels, GPUs have memory limitations that makes such simulations impossible, which is not the case with supercomputers relying on distributed memory. These “computational experiments” took place on the Prometheus supercomputer (CYFRONET, Krakow, Poland) and enabled us to test for the validity of the code on a very large scale. Results show that Tinker-HP is still operational beyond 20 million atoms. Of course, the production really slows down to a few dozen ps per day but the performance is noticeable as it would be quite enough to compute properties such as electrostatic potentials or even a short ns-scale molecular dynamics. Thus, one can expect, depending on the machine used, to produce a ns in a few weeks on the largest Ocean water box using TCG2/RESPA (3 fs). It is worth noticing that the largest computation was limited only by the computer system availability and that presently larger systems are potentially accessible with more computational resources. However, such very large computations require a different setup than the others due to memory limitations and communication issues. Indeed, for such a large number of atoms, FFTs really become severely time limiting and intra-node communications strongly affect performances. One solution that was used for Ocean was to only use a fraction of the cores of a node to take advantage of the node memory without suffering from excessive communications. That way, if the Ocean test ran on 12288 cores on 512 nodes, we used only 6 cores/node (on 24) to actually perform the computation. This gave us the possibility to better use the bandwidth of the interconnect cards (by reducing contention in MPI transfers between cores and cards), a strategy that compensates for the lack of active cores and that can be used for any system size. We used the same strategy to a lower extent for Sea as 17 cores out of 24 were active. Overall, a rough estimate for the fastest Broadwell CPU configuration (Occigen) is that using a RESPA (3 fs)/TCG2 setup, a routine production of 1 ns per day is reachable for a million atoms. Such a value is a combination of various hardware setups that are not only dependent on the CPU speed (and numbers), as the interconnection cards have a strong influence on the final results (Fig. 7).

Ionic liquids. Room temperature ionic liquids (ILs) are molten salts at room temperature that are formed by the combination of organic or inorganic cations with (generally) inorganic anions. These compounds exhibit a wide variety of useful properties that led to their use in numerous applications.^{62–65} The unusual properties observed in ILs arise from the inter- and intra-molecular interactions of the constituent ions. Thus, the computational simulations of these systems greatly benefit from the use of highly accurate potentials. Recently AMOEBA parameters for several ILs have been developed and applied for various systems.^{66–68} It is known that polarization effects result in better reproduction of transport properties.^{69–72} In addition, ILs are viscous fluids and it is thus necessary to perform relatively long MD simulations. Therefore, Tinker-HP is an ideal platform for these systems given its HPC capabilities and implementation of significantly more accurate and efficient algorithms for the evaluation of the polarization component. Indeed, ILs usually require a lot more iterations than standard molecules with standard solvers such as JOR (Jacobi Over Relaxation, see ref. 41), which is not the case with Krylov solvers such as PCG or TCG, with which such systems have been tested.^{42} As a first example, simulations were performed for 1,3-dimethylimidazolium imidazolium/chloride ([dmim^{+}][cl^{−}]) for 200 ns using the parameters reported by Starovoytov et al.^{66} The results calculated with Tinker-HP are in very good agreement with the previously reported results, with the added advantage that Tinker-HP provides excellent scaling, with production runs for a system of 216 ion pairs (in a cubic box of 35.275 Å, a PME grid of 48 × 48 × 48 and a 7 Å real space cutoff) of more than 11.5 ns per day on 240 cores. Therefore, Tinker-HP enables simulations of IL systems in the hundreds of ns up to μs timescales.

The characteristics of the inhomogeneous systems simulations boxes used for benchmark are summed up in Table 2.

Systems | Ubiquitin | Dhfr | COX-2 | STMV | Ribosome |
---|---|---|---|---|---|

Number of atoms | 9732 | 23558 | 174219 | 1066228 | 3484755 |

Size (of an edge) in Angstroms | 54.99 × 41.91 × 41.91 | 62.23 | 120 | 223 | 327.1 |

Size (of an edge) of the PME grid | 72 × 54 × 54 | 64 | 128 | 270 | 360 |

Small proteins: ubiquitin and DHFR. We started our study by testing Tinker-HP on small proteins were 3D domain decomposition is expected not be fully efficient (our water boxe study started at 96000 atoms, which is 4 times the size of DHFR and 10 times that of Ubiquitin). Surprisingly, results remain competitive with GPUs which are fully taking advantage of their computing power for such a range of systems with low memory requirements. DHFR allows to study in depth the code behavior in that system size range. Indeed, the best production time for a use of all cores of a node brings us to a 7.69 ns per day using TCG2. This production time is really close to the 8.29 ns per day exhibited by Tinker-OpenMM on a GTX1080 (see Table 3). If we used the same number of cores distributed on more nodes, to use the same technique we used on the large ocean and sea water boxes, the performance extends to 8.79 ns per day. These numbers make Tinker-HP very competitive for these small systems on a reasonable number of cores that is easily accessible on modern supercomputers. In addition, one can note that most of the recent machines use Broadwell Xeon that gives slightly better performances by a few percents. In other words, Tinker-HP is able to compensate for the computational burden of the use of double precision thanks to its new algorithmics compared to the accelerated mixed precision GPUs thus reaching both speed and accuracy. A detailed analysis of the DHFR scaling against ideal scaling is provided in ESI S2.† As one could expect, the deviation to the ideal scaling is higher than in the case of the previously larger Lake water box: larger the system is, closer to the ideal scaling we get.

Systems | Ubiquitin | DHFR | COX-2 | STMV | Ribosome |
---|---|---|---|---|---|

Number of atoms | 9737 | 23558 | 174219 | 1066628 | 3484755 |

Tinker-HP number of CPU cores | 480 | 680(960) | 2400 | 10800 | 10800 |

PCG (10^{−5} D, ASPC) |
8.4 | 6.3(7.2) | 1.6 | 0.45 | 0.18 |

TPCG2 | 10.42 | 7.81(8.93) | 1.98 | 0.56 | 0.22 |

TPCG2/RESPA (3 fs) | 15.62 | 11.71(13.39) | 2.98 | 0.84 | 0.34 |

CPU OPEN-MP | 0.43 | 0.21 | 0.024 | 0.0007 | N.A. |

GPU (GTX 1080) | 10.97 | 7.85 | 1.15 | N.A. | N.A. |

Systems (water boxes) | Puddle | Pond | Lake | Sea | Ocean |
---|---|---|---|---|---|

Number of atoms | 96000 | 288000 | 864000 | 7776000 | 23.3 × 10^{6} |

Tinker-HP number of CPU cores | 1440 | 2400 | 7200 | 7104 | 12288 |

PCG (10^{−5} D, ASPC) |
2.54 | 1.3 | 0.52 | 0.062 | 0.0077 |

TPCG2 | 3.10 | 1.59 | 0.63 | 0.076 | 0.01 |

TPCG2/RESPA (3 fs) | 4.65 | 2.38 | 0.95 | 0.11 | 0.014 |

CPU OPEN-MP | 0.050 | 0.014 | 0.003 | N.A. | N.A. |

GPU (GTX 1080) | 2.06 | 0.80 | 0.21 | N.A. | N.A. |

Larger systems: COX-2, STMV and ribosome solvated in water. For larger systems, as it was shown for the water boxes, the 3D domain decomposition speedup is taking full effect and the distributed memory approach offers an access to systems that were up to now restricted to classical non-polarizable force fields implemented in HPC packages. The benchmarks of Table 3 show that the discussion is fully transferable to non-homogeneous systems as realistic simulation times on a reasonable number of cores are reachable for the COX-2, STMV and ribosome systems allowing for meaningful simulations. The table displays a test for the COX-2 dimer (part of the Tinker benchmark suite, see https://dasher.wustl.edu/tinker/distribution/bench/) for which 1.6 ns per day are possible on 2400 cores, a computer resource that is easily accessible in supercomputer centers. If one wants to push the performances, one ns simulation can be achieved in a little more than a day on the STMV structure (taken from the NAMD website: http://www.ks.uiuc.edu/Research/namd/) which is not accessible to our GPU implementation due to memory requirements. Such a result is really extremely promising, considering that STMV encompasses more than a million atoms within the full virus structure including its full genetic materials, the whole system being fully solvated in water. Such simulations are indeed relatively recent even for classical force fields as the Schulten group only produced the first studies 10 years ago.^{75} The present extension of the simulation capabilities to advanced multipolar polarizable force fields opens new routes to the understanding of complex biosystems. Indeed, as we have seen, Tinker-HP is able to go far beyond the million atom scale and studies on the ribosome become possible following early studies (see ref. 76 and references therein). We built a model for benchmark purposes for the 70 s ribosome from Thermus thermophilus containing nearly 5000 nucleotides and over 20 proteins, with over 4100 sodium ions to neutralize the nucleic acid, and about a million water molecules for a total of 3484755 atoms. Presently, three days are necessary to produce a ns allowing for a very detailed study of such an important structure. We expect even free energy studies to be feasible. Various incoming studies will analyze more in-depth the use of PFFs to such mostly important biosystems.

In this section, we will describe the non-periodic code in Tinker-HP, based on the recently proposed ddCOSMO,^{45,46,82,83} a domain decomposition (dd) discretization of the conductor-like screening model.^{44} We will then discuss two complementary QM/MM strategies that can be used to couple Tinker-HP to a quantum-mechanical code.

The parallelization strategy adopted for the ddCOSMO implementation follows the spatial decomposition logic discussed in Section 2. Again, we divide the space occupied by the system into blocks and assign a block to each CPU. The CPU is then responsible for updating the positions, speeds and accelerations of the atoms belonging to it block. However, there are two important differences compared to the spatial decomposition discussed for short-range interactions. First, the space occupied by the solute is not a cube or a regular geometrical configuration but rather a cavity whose shape depends on the configuration of the solute. Second, the cavity is not fixed during the simulation as it evolves with the solute.

To address the first issue, we define the blocks by enclosing the solute in the smallest parallelepiped containing it and we divide this parallelepiped into smaller ones. This strategy presents the advantage of allowing us to reuse the whole machinery that has been described in Section 2. However, such a strategy can imply potential load balancing issues that require to be addressed, especially when a high number of processors is used. Again, an iterative procedure has been implemented to determine the optimal sizes of the sub-domains.

To solve the second issue, one should in principle recompute the enclosing parallelepiped at each time step. To avoid the cost of performing such an operation, we build a slightly larger parallelepiped and recompute its geometry only once every few MD steps (n = 20 for example).

In Tinker-HP, the solution to the ddCOSMO linear equations is computed by using the JI/DIIS iterative solver also used for the polarization eqn (1). The iterative procedure requires to compute MVP with the sparse ddCOSMO matrix, which can be done both very efficiently and involving only local communications. However, the right-hand side of the ddCOSMO equations depends on the electrostatic potential created by the solute's permanent and induced multipoles. In the current implementation, the potential is computed via a double loop, which implies a computational cost. Furthermore, an “all to all” communication of the positions of the system is required prior to this computation.

Thus, the computational bottleneck in terms of both computational complexity and parallel efficiency lies in the computation of the right-hand side. If AMOEBA/ddCOSMO MD simulations have been shown to be possible,^{46} this kind of boundary is not competitive with SPME in term of pure polarizable MD production. However, as we stated at the beginning of this section, the advantage of the ddCOSMO implementation is to provide a boundary condition for multiscale simulations. In particular, having non-periodic boundary conditions is ideal when working with localized basis functions in QM computations.

Detailed benchmark results of the current parallel implementation are presented in ESI S3.†

The present QM/MM possibilities of Tinker-HP follow two complementary strategies. Tinker-HP can be used as an external embedding tool, or can be directly coupled to a QM code in order to obtain a fully self-consistent polarizable QM/MM implementation.

The first strategy is the one followed in LICHEM^{77} (Layered Interacting CHEmical Model), that provides a QM/MM interface with unmodified quantum chemistry software suites such as Gaussian,^{86} PSI4,^{87} and NWChem^{88} to perform QM/MM calculations using the AMOEBA force field. This is done by approximating AMOEBA's multipolar distribution, with a set of point charges,^{89} which can then be read by the QM code. This choice is motivated by the idea of developing an interface with existing QM codes with non-native multipolar QM/MM capabilities. LICHEM extracts forces and energies from unmodified QM packages to perform a variety of calculations for non-bonded and bonded QM/MM systems, the latter by using the pseudobond formalism explicitly extended for QM/MM with PFFs.^{77,90,91} The calculations available in LICHEM include geometry and reaction path optimizations, single-point energy calculations, Monte Carlo, PIMD, etc.

Currently, the polarization component for the QM/MM interaction term in LICHEM is not fully self-consistent due to the use of unmodified QM codes. This is because only the field from the permanent multipoles from the MM subsystem is included in the effective Hamiltonian for the polarization component of the QM/MM interaction. However, as has been shown previously, this approximation, coupled with the fact that the QM and MM subsystem polarization is fully considered results into the recovery of over 80% of the total QM/MM self-consistent polarization.^{77,92}

For the computation of electronic properties and full hybrid MD simulations, a second QM/MM approach can be pursued. This approach proposes a fully self-consistent treatment of the electronic density and the MM polarization and requires a modification of the QM self-consistent field routines. A QM/AMOEBA implementation that couples Tinker-HP to a locally modified version of the Gaussian suite of programs^{86} has been recently introduced.^{80,81} Such a strategy enables to use a DFT/AMOEBA based polarizable QM/MM strategy to compute the energy and response properties of an embedded system, as well as to perform Born–Oppenheimer (BO) hybrid QM/MM MD. The latter is accelerated through the use of an extended BO Lagrangian approach (XL-BO),^{93} which provides enhanced guess for the electronic density at every time step and allows for a stable hybrid MD with enhanced energy conservation.

In short, Tinker-HP offers additional advanced QM/MM functionalities with polarizable force fields. The continuous investigation efforts in our groups have the objective to bring sampling capabilities in a multiscale polarizable environment dedicated to electronic structure as sampling has been shown to be a key issue for predictive studies.^{80}

- J. Huang, S. Rauscher, G. Nawrocki, T. Ran, M. Feig, B. L. de Groot, H. Grubmueller and A. D. MacKerell Jr, Nat. Methods, 2017, 14, 71–73 CrossRef CAS PubMed.
- J. A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K. E. Hauser and C. Simmerling, J. Chem. Theory Comput., 2015, 11, 3696–3713 CrossRef CAS PubMed.
- M. J. Robertson, J. Tirado-Rives and W. L. Jorgensen, J. Chem. Theory Comput., 2015, 11, 3499–3509 CrossRef CAS PubMed.
- M. M. Reif, P. H. Honenberger and C. Oostenbrink, J. Chem. Theory Comput., 2012, 8, 3705–3723 CrossRef CAS PubMed.
- J. W. Ponder and D. A. Case, Adv. Protein Chem., 2003, 66, 27–85 CrossRef CAS PubMed , Protein Simulations.
- B. R. Brooks, C. L. Brooks, A. D. Mackerell, L. Nilsson, R. J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A. R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R. W. Pastor, C. B. Post, J. Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D. M. York and M. Karplus, J. Comput. Chem., 2009, 30, 1545–1614 CrossRef CAS PubMed.
- A.-P. E. Kunz, J. R. Allison, D. P. Geerke, B. A. C. Horta, P. H. Hunenberger, S. Riniker, N. Schmid and W. F. van Gunsteren, J. Comput. Chem., 2012, 33, 340–353 CrossRef CAS PubMed.
- R. Salomon-Ferrer, D. A. Case and R. C. Walker, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 2013, 3, 198–210 CrossRef CAS.
- J. A. Rackers, M. L. Laury, C. Lu, Z. Wang, L. Lagardère, J.-P. Piquemal, P. Ren and J. W. Ponder, J. Comput. Chem., 2018 Search PubMed , in preparation.
- S. Plimpton, J. Comput. Phys., 1995, 117, 1–19 CrossRef CAS.
- J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kale and K. Schulten, J. Comput. Chem., 2005, 26, 1781–1802 CrossRef CAS PubMed.
- M. J. Abraham, T. Murtola, R. Schulz, S. Pall, J. C. Smith, B. Hess and E. Lindahl, SoftwareX, 2015, 1–2, 19–25 CrossRef.
- R. Salomon-Ferrer, A. W. Gotz, D. Poole, S. Le Grand and R. C. Walker, J. Chem. Theory Comput., 2013, 9, 3878–3888 CrossRef CAS PubMed.
- W. Smith and I. T. Todorov, Mol. Simul., 2006, 32, 935–943 CrossRef CAS.
- C. Kobayashi, J. Jung, Y. Matsunaga, T. Mori, T. Ando, K. Tamura, M. Kamiya and Y. Sugita, J. Comput. Chem., 2017, 38, 2193–2206 CrossRef CAS PubMed.
- K. J. Bowers, E. Chow, H. Xu, R. O. Dror, M. P. Eastwood, B. A. Gregersen, J. L. Klepeis, I. Kolossvary, M. A. Moraes, F. D. Sacerdoti, J. K. Salmon, Y. Shan and D. E. Shaw, Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters, Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, New York, NY, USA, 2006 Search PubMed.
- D. E. Shaw, R. O. Dror, J. K. Salmon, J. P. Grossman, K. M. Mackenzie, J. A. Bank, C. Young, M. M. Deneroff, B. Batson, K. J. Bowers, E. Chow, M. P. Eastwood, D. J. Ierardi, J. L. Klepeis, J. S. Kuskin, R. H. Larson, K. Lindorff-Larsen, P. Maragakis, M. A. Moraes, S. Piana, Y. Shan and B. Towles, Millisecond-scale Molecular Dynamics Simulations on Anton, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, New York, NY, USA, 2009, pp 39:1–39:11 Search PubMed.
- N. Gresh, P. Claverie and A. Pullman, Theor. Chim. Acta, 1984, 66, 1–20 CrossRef CAS.
- A. Stone, The Theory of Intermolcular Forces, Oxford Scholarship, 2013 Search PubMed.
- W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell and P. A. Kollman, J. Am. Chem. Soc., 1996, 118, 2309 CrossRef CAS.
- M. S. Gordon, M. A. Freitag, P. Bandyopadhyay, J. H. Jensen, V. Kairys and W. J. Stevens, J. Phys. Chem. A, 2001, 105, 293–307 CrossRef CAS.
- O. Engkvist, P.-O. Atrand and G. Karlstromm, Chem. Rev., 2000, 100, 4087–4108 CrossRef CAS PubMed.
- M. S. Gordon, L. Slipchenko, H. Li and J. H. Jensen, in Chapter 10 The Effective Fragment Potential: A General Method for Predicting Intermolecular Interactions, ed. D. Spellmeyer and R. Wheeler, Annual Reports in Computational Chemistry Supplement C, Elsevier, 2007, vol. 3, pp. 177–193 Search PubMed.
- P. Ren and J. W. Ponder, J. Phys. Chem. B, 2003, 107, 5933–5947 CrossRef CAS.
- G. Lamoureux and B. Roux, J. Chem. Phys., 2003, 119, 3025–3039 CrossRef CAS.
- N. Gresh, G. A. Cisneros, T. A. Darden and J.-P. Piquemal, J. Chem. Theory Comput., 2007, 3, 1960–1986 CrossRef CAS PubMed.
- W. L. Jorgensen, J. Chem. Theory Comput., 2007, 3, 1877 CrossRef CAS PubMed.
- Y. Shi, P. Ren, M. Schnieders and J.-P. Piquemal, Reviews in Computational Chemistry, John Wiley and Sons, Inc, 2015, vol. 28, pp. 51–86 Search PubMed.
- N. Gresh, K. E. Hage, E. Goldwaser, B. de Courcy, R. Chaudret, D. Perahia, C. Narth, L. Lagardère, F. Lipparini and J.-P. Piquemal, in Quantum Modeling of Complex Molecular Systems, ed. J.-L. Rivail, M. Ruiz-Lopez and X. Assfeld, Springer International Publishing, Cham, 2015, pp. 1–49 Search PubMed.
- J.-P. Piquemal and G. A. Cisneros, Many-Body Effects and Electrostatics in Biomolecules, Pan Standford, 2016, pp. 269–299 Search PubMed.
- W. Jiang, D. J. Hardy, J. C. Phillips, A. D. MacKerell, K. Schulten and B. Roux, J. Phys. Chem. Lett., 2011, 2, 87–92 CrossRef CAS PubMed.
- J. E. Stone, J. C. Phillips, P. L. Freddolino, D. J. Hardy, L. G. Trabuco and K. Schulten, J. Comput. Chem., 2007, 28, 2618–2640 CrossRef CAS PubMed.
- S. L. Grand, A. W. Gotz and R. C. Walker, Comput. Phys. Commun., 2013, 184, 374–380 CrossRef.
- M. Harger, D. Li, Z. Wang, K. Dalby, L. Lagardère, J.-P. Piquemal, J. Ponder and P. Ren, J. Comput. Chem., 2017, 38, 2047–2055 CrossRef CAS PubMed.
- P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern, R. P. Wiewiora, B. R. Brooks and V. S. Pande, PLoS Comput. Biol., 2017, 13, 1–17 Search PubMed.
- J. W. Ponder, C. Wu, P. Ren, V. S. Pande, J. D. Chodera, M. J. Schnieders, I. Haque, D. L. Mobley, D. S. Lambrecht, R. A. J. DiStasio, M. Head-Gordon, G. N. I. Clark, M. E. Johnson and T. Head-Gordon, J. Phys. Chem. B, 2007, 114, 2549–2564 CrossRef PubMed.
- J.-P. Piquemal, L. Perera, G. A. Cisneros, P. Ren, L. G. Pedersen and T. A. Darden, J. Chem. Phys., 2006, 125, 054511 CrossRef PubMed.
- K. J. Bowers, R. O. Dror and D. E. Shaw, J. Chem. Phys., 2006, 124, 184109 CrossRef PubMed.
- M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids, Clarendon Press, New York, NY, USA, 1989 Search PubMed.
- L. Lagardère, F. Lipparini, E. Polack, B. Stamm, E. Cancès, M. Schnieders, P. Ren, Y. Maday and J. P. Piquemal, J. Chem. Theory Comput., 2015, 11, 2589–2599 CrossRef PubMed.
- F. Lipparini, L. Lagardère, B. Stamm, E. Cancès, M. Schnieders, P. Ren, Y. Maday and J.-P. Piquemal, J. Chem. Theory Comput., 2014, 10, 1638–1651 CrossRef CAS PubMed.
- F. Aviat, A. Levitt, B. Stamm, Y. Maday, P. Ren, J. W. Ponder, L. Lagardère and J.-P. Piquemal, J. Chem. Theory Comput., 2017, 13, 180–190 CrossRef CAS PubMed.
- F. Aviat, L. Lagardère and J.-P. Piquemal, J. Chem. Phys., 2017, 147, 161724 CrossRef PubMed.
- A. Klamt and G. Schuurmann, J. Chem. Soc., Perkin Trans. 2, 1993, 799–805 RSC.
- F. Lipparini, B. Stamm, E. Cancès, Y. Maday and B. Mennucci, J. Chem. Theory Comput., 2013, 9, 3637–3648 CrossRef CAS PubMed.
- F. Lipparini, L. Lagardère, C. Raynaud, B. Stamm, E. Cancès, B. Mennucci, M. Schnieders, P. Ren, Y. Maday and J.-P. Piquemal, J. Chem. Theory Comput., 2015, 11, 623–634 CrossRef CAS PubMed.
- U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee and L. G. Pedersen, J. Chem. Phys., 1995, 103, 8577–8593 CrossRef CAS.
- A. Toukmaji, C. Sagui, J. Board and T. Darden, J. Chem. Phys., 2000, 113, 10913–10927 CrossRef CAS.
- C. Sagui, L. G. Pedersen and T. A. Darden, J. Chem. Phys., 2004, 120, 73–87 CrossRef CAS PubMed.
- C. Narth, L. Lagardère, É. Polack, N. Gresh, Q. Wang, D. R. Bell, J. A. Rackers, J. W. Ponder, P. Y. Ren and J.-P. Piquemal, J. Comput. Chem., 2016, 37, 494–506 CrossRef CAS PubMed.
- N. Li and S. Laizet, Cray User Group 2010 conference, Edinburgh, 2010 Search PubMed.
- M. Frigo and S. G. Johnson, Proc. IEEE, 2005, 93, 216–231 CrossRef , Special issue on “Program Generation, Optimization, and Platform Adaptation”.
- J. Kolafa, J. Comput. Chem., 2004, 25, 335–342 CrossRef CAS PubMed.
- M. Tuckerman, B. J. Berne and G. J. Martyna, J. Chem. Phys., 1992, 97, 1990–2001 CrossRef CAS.
- J. Wang, P. Cieplak, Q. Cai, M.-J. Hsieh, J. Wang, Y. Duan and R. Luo, J. Phys. Chem. B, 2012, 116, 7999–8008 CrossRef CAS PubMed.
- Y. Shi, Z. Xia, J. Zhang, R. Best, C. Wu, J. W. Ponder and P. Ren, J. Chem. Theory Comput., 2013, 9, 4046–4063 CrossRef CAS PubMed.
- A. Marjolin, C. Gourlaouen, C. Clavaguéra, P. Y. Ren, J. C. Wu, N. Gresh, J.-P. Dognon and J.-P. Piquemal, Theor. Chem. Acc., 2012, 131, 1198 CrossRef.
- A. Marjolin, C. Gourlaouen, C. Clavaguéra, P. Y. Ren, J.-P. Piquemal and J.-P. Dognon, J. Mol. Model., 2014, 20, 2471 CrossRef PubMed.
- T. A. Halgren, J. Am. Chem. Soc., 1992, 114, 7827–7843 CrossRef CAS.
- Y. Shi, Z. Xia, J. Zhang, R. Best, C. Wu, J. W. Ponder and P. Ren, J. Chem. Theory Comput., 2013, 9, 4046–4063 CrossRef CAS PubMed.
- A.-P. Hynninen and M. F. Crowley, J. Comput. Chem., 2014, 35, 406–413 CrossRef CAS PubMed.
- F. Endres and S. Z. E. Abedin, Phys. Chem. Chem. Phys., 2006, 8, 2101–2116 RSC.
- R. P. Swatloski, S. K. Spear, J. D. Holbrey and R. D. Rogers, J. Am. Chem. Soc., 2002, 124, 4974–4975 CrossRef CAS PubMed.
- H. Zhang, J. Wu, J. Zhang and J. He, Macromolecules, 2005, 38, 8272–8277 CrossRef CAS.
- D. Li, M. Wang, J. Wu, Q. Zhang, Y. Luo, Z. Yu, Q. Meng and Z. Wu, Langmuir, 2009, 25, 4808–4814 CrossRef CAS PubMed.
- O. N. Starovoytov, H. Torabifard and G. A. Cisneros, J. Phys. Chem. B, 2014, 118, 7156–7166 CrossRef CAS PubMed.
- Y.-J. Tu, M. J. Allen and G. A. Cisneros, Phys. Chem. Chem. Phys., 2016, 18, 10323–30333 Search PubMed.
- H. Torabifard, L. Reed, M. T. Berry, J. E. Jein, E. Menke and G. Cisneros, J. Chem. Phys., 2017, 147, 161731 CrossRef PubMed.
- T. Yan, C. J. Burnham, M. G. D. Pópolo and G. A. Voth, J. Phys. Chem. B, 2004, 108, 11877–11881 CrossRef CAS.
- A. Bagno, F. D'Amico and G. Saielli, J. Mol. Liq., 2007, 131–132, 17–23 CrossRef CAS.
- O. Borodin and G. D. Smith, J. Phys. Chem. B, 2006, 110, 6279–6292 CrossRef CAS PubMed.
- V. V. Chaban and O. V. Prezhdo, Phys. Chem. Chem. Phys., 2011, 13, 19345–19354 RSC.
- C. H. Bennett, J. Comput. Phys., 1976, 22, 245–268 CrossRef.
- L. Zheng, M. Chen and W. Yang, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 20227–20232 CrossRef CAS PubMed.
- P. L. Freddolino, A. S. Arkhipov, S. B. Larson, A. McPherson and K. Schulten, Structure, 2006, 14, 437–449 CrossRef CAS PubMed.
- J. R. Perilla, B. C. Goh, C. K. Cassidy, B. Liu, R. C. Bernardi, T. Rudack, H. Yu, Z. Wu and K. Schulten, Current Opinion in Structural Biology, 2015, vol. 31, pp. 64–74, Theory and simulation/Macromolecular machines and assembliesTheory and simulation/Macromolecular machines and assemblies Search PubMed.
- E. G. Kratz, A. R. Walker, L. Lagardère, F. Lipparini, J.-P. Piquemal and G. Andrés Cisneros, J. Comput. Chem., 2016, 37, 1019–1029 CrossRef CAS PubMed.
- C. Curutchet, A. Muñoz-Losa, S. Monti, J. Kongsted, G. D. Scholes and B. Mennucci, J. Chem. Theory Comput., 2009, 5, 1838–1848 CrossRef CAS PubMed.
- S. Caprasecca, S. Jurinovich, L. Lagardère, B. Stamm and F. Lipparini, J. Chem. Theory Comput., 2015, 11, 694–704 CrossRef CAS PubMed.
- D. Loco, E. Polack, S. Caprasecca, L. Lagardère, F. Lipparini, J.-P. Piquemal and B. Mennucci, J. Chem. Theory Comput., 2016, 12, 3654–3661 CrossRef CAS PubMed.
- D. Loco, L. Lagardère, S. Caprasecca, F. Lipparini, B. Mennucci and J.-P. Piquemal, J. Chem. Theory Comput., 2017, 13, 4025–4033 CrossRef CAS PubMed.
- F. Lipparini, L. Lagardère, G. Scalmani, B. Stamm, E. Cancès, Y. Maday, J.-P. Piquemal, M. J. Frisch and B. Mennucci, J. Phys. Chem. Lett., 2014, 5, 953–958 CrossRef CAS PubMed.
- F. Lipparini, G. Scalmani, L. Lagardère, B. Stamm, E. Cancès, Y. Maday, J.-P. Piquemal, M. J. Frisch and B. Mennucci, J. Chem. Phys., 2014, 141, 184108 CrossRef PubMed.
- J. Tomasi, B. Mennucci and R. Cammi, Chem. Rev., 2005, 105, 2999–3093 CrossRef CAS PubMed.
- C. Cramer and D. Truhlar, Chem. Rev., 1999, 99, 2161–2200 CrossRef CAS PubMed.
- M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. V. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. A. Montgomery Jr, J. E. Peralta, F. Ogliaro, M. J. Bearpark, J. J. Heyd, E. N. Brothers, K. N. Kudin, V. N. Staroverov, T. A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. P. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman and D. J. Fox, Gaussian 16 Revision A.03, Gaussian Inc, Wallingford CT, 2016 Search PubMed.
- R. M. Parrish, L. A. Burns, D. G. A. Smith, A. C. Simmonett, A. E. DePrince, E. G. Hohenstein, U. Bozkaya, A. Y. Sokolov, R. Di Remigio, R. M. Richard, J. F. Gonthier, A. M. James, H. R. McAlexander, A. Kumar, M. Saitow, X. Wang, B. P. Pritchard, P. Verma, H. F. Schaefer, K. Patkowski, R. A. King, E. F. Valeev, F. A. Evangelista, J. M. Turney, T. D. Crawford and C. D. Sherrill, J. Chem. Theory Comput., 2017, 13, 3185–3197 CrossRef CAS PubMed.
- M. Valiev, E. Bylaska, N. Govind, K. Kowalski, T. Straatsma, H. V. Dam, D. Wang, J. Nieplocha, E. Apra, T. Windus and W. de Jong, Comput. Phys. Commun., 2010, 181, 1477–1489 CrossRef CAS.
- M. Devereux, S. Raghunathan, D. G. Fedorov and M. Meuwly, J. Chem. Theory Comput., 2014, 10, 4229–4241 CrossRef CAS PubMed.
- Y. Zhang, T.-S. Lee and W. Yang, J. Chem. Phys., 1999, 110, 46–54 CrossRef CAS.
- J. M. Parks, H. Hu, A. J. Cohen and W. Yang, J. Chem. Phys., 2008, 129, 154106 CrossRef PubMed.
- L.-P. Wang, T. Head-Gordon, J. W. Ponder, P. Ren, J. D. Chodera, P. K. Eastman, T. J. Martinez and V. S. Pande, J. Phys. Chem. B, 2013, 117, 9956–9972 CrossRef CAS PubMed.
- A. M. N. Niklasson, P. Steneteg, A. Odell, N. Bock, M. Challacombe, C. J. Tymczak, E. Holmstrom, G. Zheng and V. Weber, J. Chem. Phys., 2009, 130, 214109 CrossRef PubMed.
- M. Müller and T. Aoki, arXiv preprint arXiv:1710.08616 2017.
- openacc, https://www.openacc.org, accessed: 2017-05-31.
- J. Huang, A. C. Simmonett, F. C. Pickard IV, A. D. MacKerell Jr and B. R. Brooks, J. Chem. Phys., 2017, 147, 161702 CrossRef PubMed.
- J.-P. Piquemal, G. A. Cisneros, P. Reinhardt, N. Gresh and T. A. Darden, J. Chem. Phys., 2006, 124, 104101 CrossRef PubMed.
- R. E. Duke, O. N. Starovoytov, J.-P. Piquemal and G. A. Cisneros, J. Chem. Theory Comput., 2014, 10, 1361–1365 CrossRef CAS PubMed.

## Footnote |

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc04531j |

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