Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human
activities such as agriculture, water purification, energy production and storage …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

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 …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning
interatomic potentials, accurately reproducing both vibrational and magnetic degrees of …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

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 …

Billion atom molecular dynamics (MD) using quantum-accurate machine-learning Spectral
Neighbor Analysis Potential (SNAP) observed long-sought high pressure BC8 phase of …

Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for
atomic modeling. While small errors are widely reported for MLIPs, an open concern is …