FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials

1 code implementation • 2 May 2024 • Thomas Plé, Olivier Adjoua, Louis Lagardère, Jean-Philip Piquemal

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations.

Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

2 code implementations • 2 Nov 2020 • Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Thibaut Very, Isabelle Dupays, Zhi Wang, Théo Jaffrelot Inizan, Frédéric Célerse, Pengyu Ren, Jay W. Ponder, Jean-Philip Piquemal

Perspectives toward the strong-scaling performance of our multi-node massive parallelization strategy, unsupervised adaptive sampling and large scale applicability of the Tinker-HP code in biophysics are discussed.

Computational Physics Distributed, Parallel, and Cluster Computing Mathematical Software Chemical Physics