Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding a...Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures.In this work,we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa.We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm,an active-learning deep neural network potential,and molecular dynamics simulations with ab initio accuracy.Among the 131,481 predicted structures,we successfully identify all experimentally known ice phases within the target pressure range,including the particularly challenging ice IV and V.These phases feature highly intricate H-bond networks,which have hindered previous efforts to fully explore ice structures.Additionally,we identify 34 new ice polymorphs that are potential candidates for experimental discovery.Notably,we predict the existence of a new stable ice phase,ice L,within the temperature range of 253–291 K and pressure range of 0.38–0.57 GPa,exhibiting a unique topology unseen in any known crystals.Our findings highlight the potential for experimental discovery of new ice phases.Furthermore,our approach can be applied to other complex systems,particularly those with network structures.展开更多
Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjec...Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.展开更多
基金supported by the National Natural Science Foundation of China(grant nos.12374217,92370118,and 22003050)the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(grant no.2024-ZD-01)+2 种基金the Fundamental Research Funds for the Central Universities,China.The calculations were supported by the National Supercomputing Center in Xi’an,Chinathe BohriumCloud platformat DP technologythe International Center for Materials Discovery(ICMD)cluster of NPU。
文摘Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures.In this work,we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa.We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm,an active-learning deep neural network potential,and molecular dynamics simulations with ab initio accuracy.Among the 131,481 predicted structures,we successfully identify all experimentally known ice phases within the target pressure range,including the particularly challenging ice IV and V.These phases feature highly intricate H-bond networks,which have hindered previous efforts to fully explore ice structures.Additionally,we identify 34 new ice polymorphs that are potential candidates for experimental discovery.Notably,we predict the existence of a new stable ice phase,ice L,within the temperature range of 253–291 K and pressure range of 0.38–0.57 GPa,exhibiting a unique topology unseen in any known crystals.Our findings highlight the potential for experimental discovery of new ice phases.Furthermore,our approach can be applied to other complex systems,particularly those with network structures.
基金financially supported by the National Natural Science Foundation of China(No.92370118)the National Science Fund for Excellent Young Scientist Fund Program(Overseas)of China,the Science and Technology Activities Fund for Overseas Researchers of Shaanxi Province,and the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(No.2024-ZD-01)。
文摘Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.
