Zobrazeno 1 - 10
of 626
pro vyhledávání: '"Shapeev, A."'
Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to ac
Externí odkaz:
http://arxiv.org/abs/2410.03484
Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized density function
Externí odkaz:
http://arxiv.org/abs/2405.12544
Autor:
Kotykhov, Alexey S., Gubaev, Konstantin, Sotskov, Vadim, Tantardini, Christian, Hodapp, Max, Shapeev, Alexander V., Novikov, Ivan S.
We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivat
Externí odkaz:
http://arxiv.org/abs/2405.07069
Autor:
Rybin, Nikita, Shapeev, Alexander
Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calc
Externí odkaz:
http://arxiv.org/abs/2403.00113
Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical properties for technological advancements. Experimental measurement of these pr
Externí odkaz:
http://arxiv.org/abs/2402.18220
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract We present an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components. It is particularly important for high entropy alloys (HEAs), where multiple principal elements c
Externí odkaz:
https://doaj.org/article/b0f0367efc264e9da90842c567d7971e
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-11 (2024)
Abstract First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically
Externí odkaz:
https://doaj.org/article/9ec8299619fc4a12a8a30b8fccbf30b3
Autor:
Jalolov, Faridun N., Podryabinkin, Evgeny V., Oganov, Artem R., Shapeev, Alexander V., Kvashnin, Alexander G.
Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards efficient ca
Externí odkaz:
http://arxiv.org/abs/2309.15868
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 work, we develop a method in which Bayesian inference is employed to co
Externí odkaz:
http://arxiv.org/abs/2309.01271
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 makes decisions regarding the number of atoms and temperature at which
Externí odkaz:
http://arxiv.org/abs/2306.13345