Crystal structure prediction at finite temperatures

作     者：Ivan A.Kruglov Alexey V.Yanilkin Yana Propad Arslan B.Mazitov Pavel Rachitskii Artem R.Oganov 

作者机构：Moscow Institute of Physics and Technology9 Institutsky laneDolgoprudny 141700Russia Dukhov Research Institute of Automatics(VNIIA)Moscow 127055Russia Emerging Technologies Research CenterXPANCEODubai Investment Park FirstDubaiUnited Arab Emirates Skolkovo Institute of Science and TechnologySkolkovo Innovation Center30-1 Bolshoy blvdMoscow 121205Russia. 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：306-313页

核心收录：

学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0702[理学-物理学] 

基　　金：I.A.K.gratefully acknowledges the financial support from the Ministry of Science and Higher Education(Agreement No.075-15-2021-606)and from the Foundation for Assistance to Small Innovative Enterprises in Science and Technology(the UMNIK program) A.B.M.thanks the Russian Science Foundation(grant No.19-73-00237)for financial support The work of A.R.O.is supported by the Russian Science Foundation(grant 19-72-30043) 

主　　题：prediction structure Crystal 

摘      要：Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solvedthis problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensivecalculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum ofthe enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchableto zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction atfinite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with aforcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomicpotential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initioaccuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalentsolid (WB), an Earth-forming silicate MgSiO_(3) (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO_(2).