Chemical and physical properties of complex materials emerge from the collective motions
of the constituent atoms. These motions are in turn determined by a variety of interatomic …

Machine-learning regression has been demonstrated to precisely emulate the potential
energy and forces that are output from more expensive electronic-structure calculations …

Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …

Emerging machine learning (ML)-based approaches provide powerful and novel tools to
study a variety of physical and chemical problems. In this contribution, we outline a universal …

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-
principles methods can approach the accuracy of the reference method at a fraction of the …

We present PyXtal_FF—a package based on Python programming language—for
developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the …

In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …

In recent years, machine learning algorithms have been widely used for constructing force
fields with an accuracy of ab initio methods and the efficiency of classical force fields. Here …

Electronic structure calculations, such as those employing Kohn–Sham density functional
theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of …