Themed collection Most popular 2018-2019 physical and theoretical chemistry articles

Two-dimensional ultraviolet spectroscopy has the potential to deliver rich structural and dynamical information on biomolecules such as DNA and proteins.

This perspective summarizes mechanistic studies on cellulose dissolution in ionic liquids, highlighting the synergistic mechanism, physicochemical aspects and future research trends.

Strong coupling of molecules with confined electromagnetic fields provides novel strategies to control chemical reactivity and spectroscopy.

The dimerisation of photosynthetic reaction centres enhanced their efficiency by improving exciton transfer from an antenna.

A predictive approach for driving down machine learning model errors is introduced and demonstrated across discovery for inorganic and organic chemistry.

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

Efficient photolabile protecting groups: how to achieve exceptional photo-triggered amino-acid delivery upon irradiation in the NIR.

Photo-driven electron transfer is faster from an electron donor guest to the harder to reduce acceptor in an asymmetric cyclophane host.

We present the in-depth determination of the magnetic properties and electronic structure of the luminescent and volatile dysprosium-based single molecule magnet [Dy₂(bpm)(fod)₆] (Hfod = 6,6,7,7,8,8,8-heptafluoro-2,2-dimethyl-3,5-octanedione, bpm = 2,2-bipyrimidine).

The density matrix renormalization group (DMRG) is a powerful method to treat static correlation.

Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.

Photocatalytic dehalogenation by a common dyestuff under aqueous conditions is driven by energy-additive absorption of two photons on the single-molecule level.

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

By blending a chiral acceptor and a sensitizer into a nematic liquid crystal, a chiral nematic liquid crystal showing amplified upconverted circularly polarized luminescence could be obtained.

The application of modern machine learning to challenges in atomistic simulation is gaining attraction.

A comprehensive theory is presented to mechanistically elucidate the long-range energy transfer between molecules strongly coupled to electromagnetic fields.

Using a text-based representation of molecules, chemical reactions are predicted with a neural machine translation model borrowed from language processing.

Quantum mechanical bond orders are obtained from integration of the exchange–correlation density between topological atoms.

The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors. The results are based on a benchmark of 1300 assays and half a million compounds.

Photocyclized intermediate formation and quasi CC twisting are the dominant processes behind the AIE.

Heterogeneous nucleation of ice from supercooled liquid water by some atmospherically relevant nucleators is enhanced by ammonium salts and suppressed by alkali halides.

The lytic polysaccharide monooxygenases (LPMOs) are copper metalloenzymes that can enhance polysaccharide depolymerization through an oxidative mechanism and hence boost generation of biofuel from e.g. cellulose. By employing density functional theory in a combination of quantum mechanics and molecular mechanics (QM/MM), we report a complete description of the molecular mechanism of LPMOs.

A novel mechanism of aggregation-induced emission defined as the restriction of the ligand dissociation–aggregation process in the nanocluster range is proposed.

The interactions of neutral aromatic ligands with cationic arginine, histidine and lysine amino acid residues have been studied with ab initio calculations, symmetry adapted perturbation theory (SAPT), and a systematic meta-analysis of X-ray structures.

We construct a robust chemistry consisting of a nearsighted neural network potential, TensorMol-0.1, with screened long-range electrostatic and van der Waals physics. It is offered in an open-source Python package and achieves millihartree accuracy and a scalability to tens-of-thousands of atoms on ordinary laptops.

Polymorphism is common in molecular crystals, whose energy landscapes usually contain many structures with similar stability, but very different physical–chemical properties. Machine-learning techniques can accelerate the evaluation of energy and properties by side-stepping accurate but demanding electronic-structure calculations, and provide a data-driven classification of the most important molecular packing motifs.

Tinker-HP is massively parallel software dedicated to polarizable molecular dynamics.

A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms.

One-dimensional diamondoid polyaniline-like nanothreads combine the outstanding mechanical properties of carbon nanotubes with the versatility of NH₂ groups.

We show that hydrothermal treatment of citric acid produces methylenesuccinic acid, which gives rise to hydrogen-bonded nano-assemblies with CND-like properties.