A novel approach to describe chemical environments in high-dimensional neural network potentials

Abstract

A central concern of molecular dynamics simulations are the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system, and have generally been calculated using either pre-defined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can Gaussian approximation potential models for tungsten. Physical Review B, 90 (10):104108, 2014. 30 XW Zhou and RE Jones. Effects of cutoff functions of Tersoff potentials on molecular dynamics simulations of thermal transport. Modelling , et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6):82-97, 2012. 32 B Yegnanarayana. Artificial neural networks. PHI Learning Pvt. Ltd., 2009. 33 Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436, 2015. 34 Balázs Csanád Csáji. Approximation with artificial neural networks. Master's thesis, Etvs Lornd University, Hungary, 2001. 35 Ajeevsing Bholoa, Steven D Kenny, and Roger Smith. A new approach to potential fitting using neural networks. Nuclear instruments and methods in physics research section B: Beam interactions with materials and atoms, 255(1):1-7, 2007. 36 Sönke Lorenz, Axel Groß, and Matthias Scheffler. Representing high-dimensional potentialenergy surfaces for reactions at surfaces by neural networks. Chemical Physics Letters, 395 (4-6):210-215, 2004. 37 Thomas B Blank, Steven D Brown, August W Calhoun, and Douglas J Doren. Neural network models of potential energy surfaces. The Journal of chemical physics, 103(10): 4129-4137, 1995. 38 Frederico V Prudente and JJ Soares Neto. The fitting of potential energy surfaces using neural networks. Application to the study of the photodissociation processes. Chemical physics letters, 287(5-6):585-589, 1998. 39 Ana Carla P Bittencourt, Frederico V Prudente, and José David M Vianna. The fitting of potential energy and transition moment functions using neural networks: transition probabilities in OH (A2Σ+ X2Π). Michael Isard, et al. Tensorflow: a system for large-scale machine learning. In OSDI, volume 16, pages 265-283, 2016. 47 John-Anders Stende. Constructing high-dimensional neural network potentials for molecular dynamics. Master's thesis, University of Oslo, Norway, 2017. 48 Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249-256, 2010.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

“…Others are unique, but prohibitively expensive to generate. Many solutions to this problem have been proposed, based on general strategies such as invariant integration, 206 parameter sharing, 351 , 419 − 421 density representations, 275 or finger printing techniques. 422 − 431 Alternatively, an NN model infers the representation from data.…”

Choosing the right molecular machine learning potential

“…43,48 The list of developed LDs for molecules is extensive. It includes, among others, BP-ACSFs (Behler-Parrinello's atom-centered symmetry functions) 70 and its ANI-AEV (atomic environment vectors) 46 and wACSF (weighted ACSF) modifications, 71 SOAP (smooth overlap of atomic positions), 43 aSLATM (atomic Spectrum of London and Axilrod-Teller-Muto), 45 FCHL (Faber-Christensen-Huang-Lilienfeld), 44 Gaussian moments, 72 spherical Bessel functions, 73,74 and descriptors used in DPMD (deep potential molecular dynamics) 47 and DeepPot-SE (DPMD-smooth edition). 75 Local descriptors can be fixed before training an MLP.…”

Choosing the right molecular machine learning potential

“…43,48 The list of developed LDs for molecules is extensive. It includes, among others, BP-ACSFs (Behler-Parrinello's atom-centered symmetry functions) 70 and its ANI-AEV (atomic environment vectors) 46 and wACSF (weighted ACSF) modifications, 71 SOAP (smooth overlap of atomic positions), 43 aSLATM (atomic Spectrum of London and Axilrod-Teller-Muto), 45 FCHL (Faber-Christensen-Huang-Lilienfeld), 44 Gaussian moments, 72 spherical Bessel functions, 73,74 and descriptors used in DPMD (deep potential molecular dynamics) 47 and DeepPot-SE (DPMD-smooth edition). 75 Local descriptors can be fixed before training an MLP.…”