US 12,094,580 B2
Neural network force field computational training routines for molecular dynamics computer simulations
Karthik Ganeshan, State College, PA (US); Karim Gadelrab, Boston, MA (US); Mordechai Kornbluth, Brighton, MA (US); and Jonathan Mailoa, Cambridge, MA (US)
Assigned to Robert Bosch GmbH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Sep. 24, 2021, as Appl. No. 17/484,448.
Prior Publication US 2023/0095631 A1, Mar. 30, 2023
Int. Cl. G16C 20/70 (2019.01); G06N 3/08 (2023.01); G16C 60/00 (2019.01)
CPC G16C 20/70 (2019.02) [G06N 3/08 (2013.01); G16C 60/00 (2019.02)] 20 Claims
OG exemplary drawing
 
1. A computational process for training a neural network force field (NNFF) configured to simulate molecular and/or atomic motion within a material system, the process comprising:
receiving molecular structure data of a molecule in the material system;
optimizing a geometry of the molecule using the molecular structure data and a density functional theory (DFT) simulation to obtain DFT optimized geometry data;
optimizing the geometry of the molecule using the molecular structure data and a classical force field (FF) simulation to obtain FF optimized geometry data; and
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 the NNFF for simulating molecular and/or atomic motion within the material system; and
training the NNFF with the NNFF training data to simulate molecular and/or atomic motion within the material system while reducing the training cost of molecular dynamics data.