NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (CoRR, 2022)

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. - Peter K. Eastman; Pavan Kumar Behara; David L. Dotson; Raimondas Galvelis; John E. Herr; Josh T. Horton; Yuezhi Mao; John D. Chodera; Benjamin P. Pritchard; Yuanqing Wang; Gianni De Fabritiis; Thomas E. Markland (CoRR, 2022)

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. - Peter K. Eastman; Raimondas Galvelis; Raúl P. Peláez; Charlles R. A. Abreu; Stephen E. Farr; Emilio Gallicchio; Anton Gorenko; Michael M. Henry; Frank Hu; Jing Huang 004; Andreas Krämer; Julien Michel; et al. (CoRR, 2023)

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (J. Chem. Inf. Model., 2023)

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. - Raúl P. Peláez; Guillem Simeon; Raimondas Galvelis; Antonio Mirarchi; Peter K. Eastman; Stefan Doerr; Philipp Thölke; Thomas E. Markland; Gianni De Fabritiis (CoRR, 2024)

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. - Peter K. Eastman; Benjamin P. Pritchard; John D. Chodera; Thomas E. Markland (CoRR, 2024)

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning. - Frank Hu; Michael S. Chen; Grant M. Rotskoff; Matthew W. Kanan; Thomas E. Markland (CoRR, 2024)

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. - Francesc Sabanés Zariquiey; Raimondas Galvelis; Emilio Gallicchio; John D. Chodera; Thomas E. Markland; Gianni De Fabritiis (J. Chem. Inf. Model., 2024)