8 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Machine Learning Materials Properties by Encoding Orbital-Projected Density of States
Paulo Pires, Pierre-Paul De Breuck, Mauro Fava +2
Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node feat…
Universal Machine Learning Interatomic Potentials are Ready for Phonons
Antoine Loew, Dewen Sun, Hai-Chen Wang +2
There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models…
Two-Dimensional Noble Metal Chalcogenides in the Frustrated Snub-Square Lattice
Hai-Chen Wang, Ahmad W. Huran, Miguel A. L. Marques +4
We study two-dimensional noble metal chalcogenides, with composition {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semi-regular two-dimensional tessel…
Symmetry-based computational search for novel binary and ternary 2D materials
Hai-Chen Wang, Jonathan Schmidt, Miguel A. L. Marques +2
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs…
Large-scale machine-learning-assisted exploration of the whole materials space
Jonathan Schmidt, Noah Hoffmann, Hai-Chen Wang +5
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures.…