The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …

Coarse-graining is a molecular modeling technique in which an atomistic system is
represented in a simplified fashion that retains the most significant system features that …

The widespread application of machine learning (ML) to the chemical sciences is making it
very important to understand how the ML models learn to correlate chemical structures with …

Condense phase molecular systems organize in wide range of distinct molecular
configurations, including amorphous melt and glass as well as crystals often exhibiting …

Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for
modeling biomolecules. However, MLCG models currently require large amounts of data …

This work focuses on the representation of model-form uncertainties in molecular dynamics
simulations in various statistical ensembles. In prior contributions, the modeling of such …

Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical
droplets and exhibit a complex behavior due to their nonlinear, multiphase, and …

We present a differentiable formalism for learning free energies that is capable of capturing
arbitrarily complex model dependencies on coarse-grained coordinates and finite …

The importance of" learning by doing" cannot be overstated, as it enables learners to
understand the subject matter effectively. This systematic review explores the use of Physics …