Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

Atom‐economic catalysts open a new era of computationally driven atomistic design of
catalysts. Rationally manipulating the structures of the catalyst with atomic‐level precision …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

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 …

The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …

With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …

Traditional force fields cannot model chemical reactivity, and suffer from low generality
without re-fitting. Neural network potentials promise to address these problems, offering …