2 papers
cond-mat.mtrl-sci2024
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders
Maciej J. Karcz, Luca Messina, Eiji Kawasaki +1
Computing atomic-scale properties of chemically disordered materials requires an efficient exploration of their vast configuration space. Traditional approaches such as Monte Carlo…
cond-mat.dis-nn2022
Semi-supervised generative approach to point-defect formation in chemically disordered compounds: application to uranium-plutonium mixed oxides
Maciej J. Karcz, Luca Messina, Eiji Kawasaki +3
Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or he…