Machine Learning Potentials Papers

Machine learning potentials papers roadmap.

1. Theories and Basics

[1] J. Behler, "Atom-centered symmetry functions for constructing high-dimensional neural network potentials", J. Chem. Phys. 134(7), 074106 (2011) [pdf]

[2] J. Behler, "First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems", Angewandte Chemie International Edition, 56(42), 12828-12840.(2017）[pdf]

[3] S. Chmiela, "Machine learning of accurate energy-conserving molecular force fields", Science Advances, 3(5). (2017) [pdf]

[4] L. Nicholas, "Hierarchical modeling of molecular energies using a deep neural network", J. Chem. Phys.148, 241715 (2018) [pdf]

2. Models

[1] M. Gastegger, C. Kauffmann, J. Behler and P. Marquetand, "Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study fo all-trans alkanes", The Journal of Chemical Physics, 144, 194110 (2016) [pdf]

[2] B. Kolb, X. Luo, X. Zhou, B. Jiang, and H. Guo, "High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)", J. Phys. Chem. Lett. 8(3), (2017) [pdf]

[3] K. Shakouri, J. Behler, J. Meyer and G. Kroes, "Accurate Neural Network Description of Surface Phonons in Reactive Gas-Surface Dynamics: N2 + Ru(0001)", J. Phys. Chem. Lett. 2017, 8, 2131−2136 [pdf]

3. Packages

[1] N. Artrith and A. Urban, "An implementation of artifical neural-network potentials for atomistic materials simulations: Performance for TiO2", Computational Materials Science 114 (2016) 135–150 [pdf]

[2] A. Khorshidi and A.A. Peterson, “Amp: A modular approach to machine learning in atomistic simulations”, Phys. Commun. 207, 310–324 (2016) [pdf]

[3] K. Yao, J. Herr, D. Toth, R. Mckintyre and J. Parkhill, "The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics",Chem. Sci., 2018, 9, 2261 [pdf]