Force Fields and Simulation Engines: Lessons Learned from Porting Amber ff14SB and ff19SB to GROMACS

Fast-Forward: Automatic Assignment and Assessment of Bonded Parameters for the Martini Force Field

A new software package, Fast-Forward, which aids parametrization of models for the widely used Martini coarse-grained force field, and offers a system-agnostic suite of tools to parametrize molecules of any size, existing in any environment.

Learning consistent molecular mechanics force fields from first principles

Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose total configuration energies into simple effective interactions whose parameters are traditionally assigned based on atom or bond types, enabli...

HessFit: Deriving Molecule-Specific Force Fields from Quantum Mechanical Hessians.

A new method to build an initial configuration for molecular modeling

Benchmarking Universal Machine Learning Force Fields for Crystal Structure Prediction of High-Energy Molecular Systems

This work tested the hypothesis that classical force-field pre-relaxation can provide a more suitable starting geometry for subsequent UMLIP relaxation on a large database of high energy molecular crystals, and revealed distinct trade-offs among the evaluated models.

Torsionator: Dihedral Parameterization and Conformer Search Made Easy

Neural network potentials (NNPs), trained on quantum mechanical (QM) data, can deliver near QM-level accuracy while being much faster than QM calculations. In this work, we introduce a new software tool, Torsionator, which currently supports MACE, OBIWAN, and UMA NNPs for energy minimization of small molecules. Torsi...