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Issue 8, 2018
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The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

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Abstract

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed “TensorMol-0.1”, is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Graphical abstract: The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

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Publication details

The article was received on 17 Nov 2017, accepted on 17 Jan 2018 and first published on 18 Jan 2018


Article type: Edge Article
DOI: 10.1039/C7SC04934J
Citation: Chem. Sci., 2018,9, 2261-2269
  • Open access: Creative Commons BY-NC license
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    The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

    K. Yao, J. E. Herr, David W. Toth, R. Mckintyre and J. Parkhill, Chem. Sci., 2018, 9, 2261
    DOI: 10.1039/C7SC04934J

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