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
Quantum transport arises from the interplay of coherent interference,impurity scattering,and inter-particle interactions[1,2].The competition between disorder and interaction leads to a transition between localized an...Quantum transport arises from the interplay of coherent interference,impurity scattering,and inter-particle interactions[1,2].The competition between disorder and interaction leads to a transition between localized and delocalized phases,and many breakthroughs have been made[3].While traditional theories focus on electronic systems,recent advancements in quantum technologies,such as Bose-Einstein condensates[4],superconducting qubits[5],and trapped ions[6],enable direct simulations of quantum transport.These systems provide new opportunities to explore quantum transport beyond traditional theories,offering direct insights into particle distribution in space and time[7].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.
基金supported by the Excellent Young Scientists Fund Program(Overseas)of Chinathe National Natural Science Foundation of China(12274034,12020101003,92250301,and 12250710126)+1 种基金the China Postdoctoral Science Foundation(Y24PJ2425214)the strong support from the State Key Laboratory of Low-Dimensional Quantum Physics at Tsinghua University。
文摘Quantum transport arises from the interplay of coherent interference,impurity scattering,and inter-particle interactions[1,2].The competition between disorder and interaction leads to a transition between localized and delocalized phases,and many breakthroughs have been made[3].While traditional theories focus on electronic systems,recent advancements in quantum technologies,such as Bose-Einstein condensates[4],superconducting qubits[5],and trapped ions[6],enable direct simulations of quantum transport.These systems provide new opportunities to explore quantum transport beyond traditional theories,offering direct insights into particle distribution in space and time[7].
