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cond-mat.stat-mech2026
Equivariant learning of a transferable three-dimensional classical density functional
Bingqing Cheng
Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate ato…
physics.comp-ph2026
Distilling latent electrostatics from foundation machine learning interatomic potentials
Xiaoyu Wang, Bingqing Cheng
Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally…
cond-mat.mtrl-sci2026
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…