Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations

Abstract

Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

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

“…Statistical models from machine learning experienced growing popularity in many areas of chemistry, such as in reducing the cost of simulating chemical systems, 1 – 13 improving the accuracy of quantum methods, 14 – 22 generating force field parameters, 23 , 24 predicting molecular properties 25 – 32 and designing new materials. 33 – 38 Neural network model chemistries (NNMCs) are one of the most powerful methods among this class of models.…”

Making machine learning a useful tool in the accelerated discovery of transition metal complexes

“…The shift has been made possible by the availability of ever-faster hardware and methods [80][81][82][83][84][85][86][87][88][89][90][91][92][93] over the past couple of decades along with workflows that enable the automated simulation of molecules 49,94 and materials. 46,47,95,96 The resulting large data sets, 1,2 along with widely available open source tools 97,98 for ML model training, have in large part ushered in the advances in the application of ML for the replacement 3,6,7,43,44,[99][100][101][102][103][104] or augmentation 99,[105][106][107][108][109] of traditional theoretical chemistry for property prediction. My own group's entrance into ML model development for open-shell transition metal chemistry is an example of this trend.…”

Machine Learning Assisted Free Energy Simulation of Solution–Phase and Enzyme Reactions

“…Recently, Yang, [40][41][42] Gastegger, 43 York, 44 Riniker 45 and coworkers have proposed machine learning (ML) as a new strategy to address the computational cost of direct ai-QM/MM free energy simulations. Specifically, for configurations of interest, artificial neural network (ANN) models were designed and trained to reproduce either the ai-QM/MM potential, i.e., the machine learning potential (MLP), or the difference between the ai-QM/MM and se-QM/MM potentials, hereafter, referred to as the delta machine learning potential (DMLP).…”

“…These MLPs/DMLPs led to fairly accurate free energy barriers, with errors of around 1.0 kcal/mol, for several solution-phase reactions. [40][41][42]44 In these approaches, however, the effects of solvent on the reacting system were rather homogeneous, and their applicability to reactions in a heterogeneous solvent environment, such as in enzyme, has not been fully explored.…”