6 citations · 11 across the 4 of their papers we have counts for
4 papers
CrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
Krit Tangsongcharoen, Teerachote Pakornchote, Chayanon Atthapak +6
Determining whether a candidate crystalline material is thermodynamically stable depends on identifying its true ground-state structure, a central challenge in computational materi…
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling
Teerachote Pakornchote, Natthaphon Choomphon-anomakhun, Sorrjit Arrerut +4
The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symme…
StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks
Teerachote Pakornchote, Annop Ektarawong, Thiparat Chotibut
Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic scale ab initio approaches are compu…
Helium-hydrogen immiscibility at high pressures
Yu Wang, Xiao Zhang, Shuqing Jiang +6
Hydrogen and helium are the most abundant elements in the universe and they constitute the interiors of gas giant planets. Thus, their equations of states, phase, chemical state, a…