5 papers
Machine Learning Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation
Wenhao Zhang, Jean-Claude Crivello, Yusuke Matsuoka +2
To accurately determine phase boundaries and phase transitions, thermodynamic models that describe free energies of phases often have to be optimized based on experimentally observ…
Data-Driven Prediction of NaCl-Type Entropy-Stabilized Oxide Compositions from First-Principles and Supervised Learning
Sebastien Junier, Celine Barreteau, David Berardan +2
Entropy-stabilized oxides (ESOs) open access to vast multicomponent compositional spaces, but identifying promising candidates remains challenging because of the large number of po…
Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
Auguste de Lambilly, Vladimir Baturin, David Portehault +4
Generative diffusion models have emerged as powerful tools for the discovery of inorganic crystal structures, yet steering their sampling process toward user-defined physical and c…
Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography
Mariano Forti, Alesya Malakhova, Yury Lysogorskiy +5
Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly chal…
Randomness in atomic disorder and consequent squandering of spin-polarization in a ferromagnetically fragile quaternary Heusler alloy FeRuCrSi
Shuvankar Gupta, Sudip Chakraborty, Vidha Bhasin +7
RuFeCrSi ( 0 x 1) system is theoretically predicted to be one of the very few known examples of robust half-metallic ferromagnet with 100\% spin polarization. Si…