Molecular dynamics (MD) is a computer simulation technique that helps to explore the behavior and properties of molecules and atoms. MD has been used in research and development in many spaces, including materials sci...Molecular dynamics (MD) is a computer simulation technique that helps to explore the behavior and properties of molecules and atoms. MD has been used in research and development in many spaces, including materials science and engineering and nanotechnology. MD has been proven useful in topics like the nano-engineering of construction materials, correcting graphene planar defects, studying self-assembling bio-materials, and the densification, consolidation, and sintering of nanocrystalline materials.展开更多
A number of (1) Ph.D. graduate research fellowships, (2) postdoctoral fellowships, and (3) undergraduate research fellowships are available immediately in the area of neutron-diffraction materials research. Candidates...A number of (1) Ph.D. graduate research fellowships, (2) postdoctoral fellowships, and (3) undergraduate research fellowships are available immediately in the area of neutron-diffraction materials research. Candidates with strong backgrounds in Materials Science, Metallurgy (including Processing, and Mechanical/Physical Behavior), Computational Materials Science, Mechanical/Civil Engineering, Physics, Computer Science and Engineering, or a related field are encouraged to apply. The research activities will focus on the subjects of (1) in-situ neutron-diffraction characterization of mechanical behavior (plasticity, twinning, fatigue, and creep deformation) of advanced ma-展开更多
The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse chal...The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.展开更多
文摘Molecular dynamics (MD) is a computer simulation technique that helps to explore the behavior and properties of molecules and atoms. MD has been used in research and development in many spaces, including materials science and engineering and nanotechnology. MD has been proven useful in topics like the nano-engineering of construction materials, correcting graphene planar defects, studying self-assembling bio-materials, and the densification, consolidation, and sintering of nanocrystalline materials.
文摘A number of (1) Ph.D. graduate research fellowships, (2) postdoctoral fellowships, and (3) undergraduate research fellowships are available immediately in the area of neutron-diffraction materials research. Candidates with strong backgrounds in Materials Science, Metallurgy (including Processing, and Mechanical/Physical Behavior), Computational Materials Science, Mechanical/Civil Engineering, Physics, Computer Science and Engineering, or a related field are encouraged to apply. The research activities will focus on the subjects of (1) in-situ neutron-diffraction characterization of mechanical behavior (plasticity, twinning, fatigue, and creep deformation) of advanced ma-
基金This work was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project no.15222018).
文摘The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.
