Machine learning potentials represent a transformative bridge between empirical force fields and fully fledged quantum-mechanical simulations, offering near ab initio accuracy at a fraction of the ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Two-dimensional Group-III nitrides (h-BN, h-AlN, h-GaN, and h-InN) exhibit great promise for electronic and optoelectronic applications due to their hexagonal structures, thermal stability, and wide ...