Considerations in the use of ML interaction potentials for free energy calculations

arXiv:2403.13952v1 Announce Type: cross
Abstract: Machine learning potentials (MLPs) offer the potential to accurately model the energy and free energy landscapes of molecules with the precision of quantum mechanics and an efficiency similar to classical simulations. This research focuses on using equivariant graph neural networks MLPs due to their proven effectiveness in modeling equilibrium molecular trajectories. A key issue addressed is the capability of MLPs to accurately predict free energies and transition states by considering both the energy and the …

abstract arxiv cond-mat.mtrl-sci cs.lg efficiency energy free graph graph neural networks machine machine learning molecules networks neural networks physics.chem-ph precision quantum quantum mechanics research simulations type