239 citations · 676 across the 20 of their papers we have counts for
4 papers · 1 filter
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
Stephen E. Farr, Stefan Doerr, Antonio Mirarchi +2
We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to D…
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
Gianni De Fabritiis
Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore thei…
TorchMD: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewsk, Adrià Pérez +5
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved…
Determination of the chemical potential using energy-biased sampling
R. Delgado-Buscalioni, G. De Fabritiis, P. V. Coveney
An energy-biased method to evaluate ensemble averages requiring test-particle insertion is presented. The method is based on biasing the sampling within the subdomains of the test-…