Publications (28)
Accurate representation of formation energies of crystalline alloys with many components
Alexander Shapeev
In this paper I propose a new model for representing the formation energies of multicomponent crystalline alloys as a function of atom types. In the cases when displacements of ato…
Bayesian inference of composition-dependent phase diagrams
Timofei Miryashkin, Olga Klimanova, Vladimir Ladygin +1
Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this w…
AI-accelerated Materials Informatics Method for the Discovery of Ductile Alloys
Ivan Novikov, Olga Kovalyova, Alexander Shapeev +1
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that…
Accurate melting point prediction through autonomous physics-informed learning
Olga Klimanova, Timofei Miryashkin, Alexander Shapeev
We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method…
Thermal Conductivity and Temperature-Induced Band Gap Renormalization in Crystalline and Amorphous GaO
Rustam Arabov, Jiaxuan Li, Xiaotong Chen +2
The lattice thermal conductivity (LTC) and electron-phonon interactions in crystalline and amorphous gallium oxide are herein determined by coupling a machine-learned interatomic p…
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev +1
Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires eithe…