Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in ...Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.展开更多
The distribution of trivalent and tetravalent cations in several ATxV6-xO11 compounds was quantitatively studied by the bond valence method. In SrV6O11, the M(3) sites were mainly occupied by trivaient cations; while ...The distribution of trivalent and tetravalent cations in several ATxV6-xO11 compounds was quantitatively studied by the bond valence method. In SrV6O11, the M(3) sites were mainly occupied by trivaient cations; while M(1) and M(2) sites were shared by trivalent and tetravalent cations, the relative content of tetravalent at M(1) sites was higher than at M(2) sites. During substitution process, the trivalent cations preferred to occupy M(3) sites, tetravalent ones preferred to occupy M(2) sites. The occupancy of trivalent and tetravalent cations at M sites would change with the substitution展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFA1402304)the National Natural Science Foundation of China(Grant Nos.12034009,12374005,52288102,52090024,and T2225013)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for JLU Science and Technology Innovative Research Team.
文摘Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.
文摘The distribution of trivalent and tetravalent cations in several ATxV6-xO11 compounds was quantitatively studied by the bond valence method. In SrV6O11, the M(3) sites were mainly occupied by trivaient cations; while M(1) and M(2) sites were shared by trivalent and tetravalent cations, the relative content of tetravalent at M(1) sites was higher than at M(2) sites. During substitution process, the trivalent cations preferred to occupy M(3) sites, tetravalent ones preferred to occupy M(2) sites. The occupancy of trivalent and tetravalent cations at M sites would change with the substitution
