The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

Point defects and impurities strongly affect the physical properties of materials and have a
decisive impact on their performance in applications. First-principles calculations have …

Phonon plays essential roles in dynamical behaviors and thermal properties, which are
central topics in fundamental issues of materials science. The importance of first principles …

Multicomponent alloys with multiple principal elements including high entropy alloys (HEAs)
and compositionally complex alloys (CCAs) are attracting rapidly growing attention. The …

We investigate the thermodynamic properties of the prototype equi-atomic high entropy alloy
(HEA) CoCrFeMnNi by using finite-temperature ab initio methods. All relevant free energy …

We show that the Gaussian Approximation Potential (GAP) machine-learning framework can
describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a …

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

We propose a machine-learning interatomic potential for multi-component magnetic
materials. In this potential we consider magnetic moments as degrees of freedom (features) …

Identifying the forces that drive a phase transition is always challenging. The hcp-fcc phase
transition that occurs in cobalt at~ 700 K has not yet been fully understood, although early …

The unique and unanticipated properties of multiple principal component alloys have
reinvigorated the field of alloy design and drawn strong interest across scientific disciplines …