Located at the intersection of intriguing material phases and potentially superior mechanical
properties, high-entropy alloys (HEAs) have been gaining increasing interest across …

The transport properties of a series of refractory high-entropy alloys (RHEAs) with single-
phase body-centered cubic structures are investigated based on both experiments and first …

Boron arsenide, considered an ideal semiconductor, inevitably introduces arsenic defects
during crystal growth. Here, we develop a unified neural network interatomic potential with …

Being progressively applied in the design of highly active catalysts for energy devices,
machine learning (ML) technology has shown attractive ability of dramatically reducing the …

The concept of local lattice distortion (LLD) is of fundamental importance in the
understanding of properties of high-entropy alloys and, more generally, of multi-principal …

Phase transition is one of the most important phenomena in nature and plays a central role
in materials design. All phase transitions are characterized by suitable order parameters …

The phase stability of a bcc AlNbTiV high-entropy alloy at elevated temperatures is studied
using a combination of machine-learning interatomic potentials, first-principles calculations …

Availability of affordable and widely applicable interatomic potentials is the key needed to
unlock the riches of modern materials modeling. Artificial neural network-based approaches …

We utilize a machine-learning force field, trained by a neural network (NN) with bispectrum
coefficients as descriptors, to investigate the chemical short-range order (SRO) influences …

We extend the small set of ordered structures (SSOS) method to calculate several typical
materials properties of high entropy alloys (HEAs) with short range order (SRO). Using both …