The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties,rendering them highly promising for applications in catalysis,medicine,and battery te...The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties,rendering them highly promising for applications in catalysis,medicine,and battery technology,among other fields.Since not all materials can be synthesized into an amorphous structure,the composition design of amorphous materials holds significant importance.Machine learning offers a valuable alternative to traditional“trial-anderror”methods by predicting properties through experimental data,thus providing efficient guidance in material design.In this study,we develop a machine learning workflow to predict the critical casting diameter,glass transition temperature,and Young's modulus for 45 ternary reported amorphous alloy systems.The predicted results have been organized into a database,enabling direct retrieval of predicted values based on compositional information.Furthermore,the applications of high glass forming ability region screening for specified system,multi-property target system screening and high glass forming ability region search through iteration are also demonstrated.By utilizing machine learning predictions,researchers can effectively narrow the experimental scope and expedite the exploration of compositions.展开更多
Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the ...Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
Discovery of a new superconductor with distinct crystal structure and chemistry often provides great opportunity for further expanding superconductor material base,and also leads to better understanding of superconduc...Discovery of a new superconductor with distinct crystal structure and chemistry often provides great opportunity for further expanding superconductor material base,and also leads to better understanding of superconductivity mechanisms.Here,we report the discovery of superconductivity in a new intermetallic oxide Hf_(3)Pt_Ge_(2)O synthesized through a solid-state reaction.The Hf_(3)Pt_Ge_(2)O crystallizes in a cubic structure(space group Fm-3 m)with a lattice constant of a=1.241 nm,whose stoichiometry and atomic structure are determined by electron microscopy and x-ray diffraction techniques.The superconductivity at 4.1 K and type-II superconducting nature are evidenced by the electrical resistivity,magnetic susceptibility,and specific heat measurements.The intermetallic oxide Hf_(3)Pt_Ge_(2)O system demonstrates an intriguing structural feature that foreign oxygen atoms can be accommodated in the interstitial sites of the ternary intermetallic framework.We also successfully synthesized a series of Hf_(3)Pt_Ge_(2)O1+δ(-0.25≤δ≤0.5),and found theδ-dependent superconducting transition temperature Tc.The atomic structure and the electronic structure are also substantiated by first-principles calculations.Our results present an entirely new family of superconductors with distinct structural and chemical characteristics,and could attract research interest in further finding new superconductors and exploring novel physics pertaining to the 5 d-electron in these intermetallic compound systems.展开更多
基金Project supported by funding from the National Natural Science Foundation of China(Grant Nos.52172258,52473227 and 52171150)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0500200)。
文摘The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties,rendering them highly promising for applications in catalysis,medicine,and battery technology,among other fields.Since not all materials can be synthesized into an amorphous structure,the composition design of amorphous materials holds significant importance.Machine learning offers a valuable alternative to traditional“trial-anderror”methods by predicting properties through experimental data,thus providing efficient guidance in material design.In this study,we develop a machine learning workflow to predict the critical casting diameter,glass transition temperature,and Young's modulus for 45 ternary reported amorphous alloy systems.The predicted results have been organized into a database,enabling direct retrieval of predicted values based on compositional information.Furthermore,the applications of high glass forming ability region screening for specified system,multi-property target system screening and high glass forming ability region search through iteration are also demonstrated.By utilizing machine learning predictions,researchers can effectively narrow the experimental scope and expedite the exploration of compositions.
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
基金the National Key Research and Development Program of China(Grant Nos.2016YFA0300303,2017YFA0504703,2017YFA03029042017YFA0303000)+4 种基金the National Basic Research Program of China(Grant No.2015CB921304)the National Natural Science Foundation of China(Grant Nos.11774391,11774403,and 11804381)the Strategic Priority Research Program(B)of the Chinese Academy of Sciences(Grant Nos.XDB25000000and XDB07020000)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(Grant No.ZDKYYQ20170002)the China Postdoctoral Science Foundation(Grant No.BX20180351)。
文摘Discovery of a new superconductor with distinct crystal structure and chemistry often provides great opportunity for further expanding superconductor material base,and also leads to better understanding of superconductivity mechanisms.Here,we report the discovery of superconductivity in a new intermetallic oxide Hf_(3)Pt_Ge_(2)O synthesized through a solid-state reaction.The Hf_(3)Pt_Ge_(2)O crystallizes in a cubic structure(space group Fm-3 m)with a lattice constant of a=1.241 nm,whose stoichiometry and atomic structure are determined by electron microscopy and x-ray diffraction techniques.The superconductivity at 4.1 K and type-II superconducting nature are evidenced by the electrical resistivity,magnetic susceptibility,and specific heat measurements.The intermetallic oxide Hf_(3)Pt_Ge_(2)O system demonstrates an intriguing structural feature that foreign oxygen atoms can be accommodated in the interstitial sites of the ternary intermetallic framework.We also successfully synthesized a series of Hf_(3)Pt_Ge_(2)O1+δ(-0.25≤δ≤0.5),and found theδ-dependent superconducting transition temperature Tc.The atomic structure and the electronic structure are also substantiated by first-principles calculations.Our results present an entirely new family of superconductors with distinct structural and chemical characteristics,and could attract research interest in further finding new superconductors and exploring novel physics pertaining to the 5 d-electron in these intermetallic compound systems.
