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
Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep le...Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep learning-based algorithms have been proposed.However,current efforts primarily focus on developing models that support specific applications,with less emphasis on building unified frameworks that predict chemical reactions.Here,we developed Bidirectional Chemical Intelligent Net(Bi CINet),a prediction framework based on Bidirectional and Auto-Regressive Transformers(BARTs),for predicting chemical reactions in various tasks,including the bidirectional prediction of organic synthesis and enzyme-mediated chemical reactions.This versatile framework was trained using general chemical reactions and achieved top-1 forward and backward accuracies of 80.7%and 48.6%,respectively,for the public benchmark dataset USPTO_50K.By multitask transfer learning and integrating various task prompts into the model,Bi CINet enables retrosynthetic planning and metabolic prediction for small molecules,as well as retrosynthetic analysis and enzyme-catalyzed product prediction for natural products.These results demonstrate the superiority of our multifunctional framework for comprehensively understanding chemical reactions.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
基金financially supported by the National Natural Science Foundation of China(NSFC,No.82073692)CAMS Innovation Fund for Medical Sciences(CIFMS,No.2021-I2M-1-028)。
文摘Chemical reactions,which transform one set of substances to another,drive research in chemistry and biology.Recently,computer-aided chemical reaction prediction has spurred rapidly growing interest,and various deep learning-based algorithms have been proposed.However,current efforts primarily focus on developing models that support specific applications,with less emphasis on building unified frameworks that predict chemical reactions.Here,we developed Bidirectional Chemical Intelligent Net(Bi CINet),a prediction framework based on Bidirectional and Auto-Regressive Transformers(BARTs),for predicting chemical reactions in various tasks,including the bidirectional prediction of organic synthesis and enzyme-mediated chemical reactions.This versatile framework was trained using general chemical reactions and achieved top-1 forward and backward accuracies of 80.7%and 48.6%,respectively,for the public benchmark dataset USPTO_50K.By multitask transfer learning and integrating various task prompts into the model,Bi CINet enables retrosynthetic planning and metabolic prediction for small molecules,as well as retrosynthetic analysis and enzyme-catalyzed product prediction for natural products.These results demonstrate the superiority of our multifunctional framework for comprehensively understanding chemical reactions.
