The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics

TL;DR

TensorMol-0.1 is a hybrid neural network model that combines machine learning with long-range physics to efficiently simulate chemical systems with near ab-initio accuracy, enabling scalable molecular dynamics and other simulations.

Contribution

This work introduces TensorMol-0.1, an open-source hybrid model combining neural networks with physics-based long-range interactions for accurate and scalable chemical simulations.

Findings

Achieves millihartree accuracy in energy calculations

Scales to tens of thousands of atoms on standard laptops

Successfully reproduces vibrational spectra and simulates protein dynamics

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, and open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating 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 molecular dynamics of a protein. Our comparisons with electronic structure theory and experiment demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulate chemistry.