Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems

05/07/2019
by   Jonathan P. Mailoa, et al.
0

Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and the network-feature spatial derivatives which are computationally expensive. We develop a staggered NNFF architecture exploiting both rotation-invariant and covariant features separately to directly predict atomic force vectors without using spatial derivatives, thereby reducing expensive structural feature calculation by 180-480x. This acceleration enables us to develop NNFF which directly predicts atomic forces in complex ternary and quaternary-element extended systems comprised of long polymer chains, amorphous oxide, and surface chemical reactions. The staggered rotation-invariant-covariant architecture described here can also directly predict complex covariant vector outputs from local physical structures in domains beyond computational material science.

READ FULL TEXT
research
03/30/2017

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

Empirical scoring functions based on either molecular force fields or ch...
research
11/19/2021

Spherical harmonic shape descriptors of nodal force demands for quantifying spatial truss connection complexity

The connections of a spatial truss structure play a critical role in the...
research
06/09/2020

Simple and efficient algorithms for training machine learning potentials to force data

Abstract Machine learning models, trained on data from ab initio quantum...
research
08/13/2021

Efficient force field and energy emulation through partition of permutationally equivalent atoms

Kernel ridge regression (KRR) that satisfies energy conservation is a po...
research
02/14/2023

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

Atomic partial charges are crucial parameters in molecular dynamics (MD)...
research
03/02/2021

ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

With massive amounts of atomic simulation data available, there is a hug...
research
06/29/2022

Spherical Channels for Modeling Atomic Interactions

Modeling the energy and forces of atomic systems is a fundamental proble...

Please sign up or login with your details

Forgot password? Click here to reset