Neural network force field computational training routines for molecular dynamics computer simulations
US12094580B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 24, 2021 |
| Grant date | Sep 17, 2024 |
| Priority date | — |
| Expiry date | Dec 21, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16C10/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computational method for training a neural network force field (NNFF) configured to simulate molecular and/or atomic motion within a material system. The method includes the step of receiving molecular structure data of a molecule in the material system. The method also includes optimizing a geometry of the molecule using the molecular structure data and a density functional theory (DFT) simulation to obtain DFT optimized geometry data. The method further includes optimizing the geometry of the molecule using the molecular structure data and a classical force field (FF) simulation to obtain FF optimized geometry data. The method also includes outputting NNFF training data comprised of the DFT optimized geometry data and the FF optimized geometry data. The NNFF training data is configured to train an NNFF for simulating molecular and/or atomic molecular and/or atomic motion within the material system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.