Enhanced sampling algorithms have emerged as powerful methods to extend the utility of
molecular dynamics simulations and allow the sampling of larger portions of the …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Molecular self-organization driven by concerted many-body interactions produces the
ordered structures that define both inanimate and living matter. Here we present an …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

The development of enhanced sampling methods has greatly extended the scope of
atomistic simulations, allowing long-time phenomena to be studied with accessible …

Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe timescale limitations. To address this …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Metadynamics is a popular enhanced sampling algorithm for computing the free energy
landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary …

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …

Identifying a reduced set of collective variables is critical for understanding atomistic
simulations and accelerating them through enhanced sampling techniques. Recently …