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

By Kun Yao, John E. Herr, David W Toth, Ryker Mckintyre, John A. Parkhill. 2017

327 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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In Computational Theory and Mathematics

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Recent advances and applications of deep learning methods in materials science (2021, unknown): “com/jparkhill/TensorMol [99] PROPhet Sol https://github.”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, methodology): “1) [266] [10] [219] [158] [140] [182] [203] [76] [86] [130] [174] [204] [231] [313] [121] [125] [187] [92] [306] [180] [298] [184] [267] [39] [247] [133] [130] [231] [298] [173] [66] [24] [255] [12] [17] [203] [204] [213] [239] [125] [281] [214] [176] [249] [310] [309] [311] [245] [78] [266] [297] [”
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python (2019, background): “…and more accurate (Behler & Parrinello, 2007; Behler, 2011; Artrith et al., 2011; Artrith & Behler, 2012; Artrith et al., 2018; Deringer et al., 2018a; Bartók et al., 2018; Yao et al., 2018; Seko et al., 2015; Deringer et al., 2018b; Cubuk et al., 2017a; Faber et al., 2017; Gilmer et al., 2017).”
Modeling the formation and growth of atmospheric molecular clusters: A review (2020, methodology): “To use descriptors based just on Cartesian coordinates, Yao et al. (2018) use in their work neural networks.”
Outsmarting Quantum Chemistry Through Transfer Learning (2018, background): “Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces 23–27 , forces 28–30 , atomization energies 31,32 , atomic partial charges 32– 35 , molecular dipoles 26,36,37 , materials discovery 38–40 , and protein-ligand complex scoring 41 .”
Hamiltonian Neural Networks (2019, background): “Physicists, particularly those studying molecular dynamics, have seen success using neural networks to model energy surfaces [3, 11, 36, 44].”
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021, background): “78 These empirical augmentations allow SpookyNet to extrapolate beyond the data it was trained on based on physical knowledge from data.”
Machine learning for heterogeneous catalyst design and discovery (2018, unknown): “With the growing availability of software for machine learning potentials such as AMP, 99 PROPhet, 100 and TensorMol 101 it is evident that MLPs will keep being extended.”