Chemical reaction networks form the heart of microkinetic models, which are one of the key
tools available for gaining detailed mechanistic insight into heterogeneous catalytic …

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

The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …

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

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility,
to sample the transitions between metastable states of the free-energy landscape …

Protein thermodynamics is intimately tied to biological function and can enable processes
such as signal transduction, enzyme catalysis, and molecular recognition. The relative free …

Enhanced sampling simulations make the computational study of rare events feasible. A
large family of such methods crucially depends on the definition of some collective variables …

Identifying optimal collective variables to model transformations using atomic-scale
simulations is a long-standing challenge. We propose a new method for the generation …

Machine learning (ML) is transforming the investigation of complex biological processes. In
enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) …

We present Deep learning for Collective Variables (DeepCV), a computer code that provides
an efficient and customizable implementation of the deep autoencoder neural network …