[教授本人][招生][PhD/MS/Intern]纽约州立大学布法罗分校 AI for materials/chemistry
5570
导师简介:
彭嘉宇教授于2017从中国科学技术大学取得应用物理学本科学位，于2022年取得美国麻省理工学院（MIT）材料科学与工程博士学位，此后于MIT材料系担任博士后研究员。2025年1月起，将在美国纽约州立大学布法罗分校的材料设计与创新系担任助理教授。彭嘉宇教授课题组主要研究方向将为结合物理驱动和数据驱动的新材料设计，相关研究成果以第一作者或通讯作者发表在Nature Materials, Nature Review Materials, Joule等高水平期刊。课题组将招收多位2025秋季入学的研究生或博士生，如感兴趣，欢迎访问课题组主页（jiayu-peng.com）并通过Join Us页面中的联系方式发邮件讨论招生机会。
课题组方向：
We are an interdisciplinary computational research group in the Department of Materials Design and Innovation at the University at Buffalo. We aspire to combine data science and machine learning with materials physics and surface chemistry to elucidate new physical principles and accelerate materials discovery for transformative opportunities in decarbonization and sustainability. Our work focuses on understanding and optimizing the formidable structural, compositional, and mechanistic complexities of materials and interfaces under various essential reaction conditions relevant to negative emissions science and renewable energy technologies, with a center on electrochemistry and catalysis. We build physics-informed, data-driven machine-learning methods to capture the fundamental laws of materials thermodynamics and surface kinetics from atomistic simulations and characterization data and empower all group members to construct materials-centric solutions for the most urgent societal challenges, such as climate change, pollution, energy poverty, and food insecurity.
招生信息：
We have multiple openings for self-motivated Ph.D. and Master students to join our research group in Fall 2025! Our group is part of a highly 1point3acres.com with engineering.buffalo.edu at the intersection of materials science and data science. We aim to train graduate students with transdisciplinary skillsets across materials modeling, electrochemistry, catalysis, surface science, data analytics, and machine learning to meet the career demands of an increasingly data-driven and cross-disciplinary world. While we are primarily a computational research group, we are deeply interested in having our students work with experimental collaborators to integrate high-throughput simulations and physics-driven machine learning to facilitate efficient, accurate, and rigorous elucidation of new atomistic insights and physical principles from convoluted experimental characterization data.
Prospective students interested in joining our group are encouraged to directly email Jiayu (1point3acres.com) with an attached CV and transcript and use the email subject line “Prospective Student – [Your Name].” In your email, please also provide a brief summary of your research interests and career goals. We welcome new students from diverse academic backgrounds, including but not limited to materials science, chemistry, physics, chemical engineering, mechanical engineering, and computational and information science. Past academic experience in any of the following subjects is helpful: solid-state physics, physical chemistry, thermodynamics, statistical mechanics, or deep learning, but no prior computational modeling experience is necessary as long as you are highly motivated to learn and apply new skills in our group.
彭嘉宇教授于2017从中国科学技术大学取得应用物理学本科学位，于2022年取得美国麻省理工学院（MIT）材料科学与工程博士学位，此后于MIT材料系担任博士后研究员。2025年1月起，将在美国纽约州立大学布法罗分校的材料设计与创新系担任助理教授。彭嘉宇教授课题组主要研究方向将为结合物理驱动和数据驱动的新材料设计，相关研究成果以第一作者或通讯作者发表在Nature Materials, Nature Review Materials, Joule等高水平期刊。课题组将招收多位2025秋季入学的研究生或博士生，如感兴趣，欢迎访问课题组主页（jiayu-peng.com）并通过Join Us页面中的联系方式发邮件讨论招生机会。
课题组方向：
We are an interdisciplinary computational research group in the Department of Materials Design and Innovation at the University at Buffalo. We aspire to combine data science and machine learning with materials physics and surface chemistry to elucidate new physical principles and accelerate materials discovery for transformative opportunities in decarbonization and sustainability. Our work focuses on understanding and optimizing the formidable structural, compositional, and mechanistic complexities of materials and interfaces under various essential reaction conditions relevant to negative emissions science and renewable energy technologies, with a center on electrochemistry and catalysis. We build physics-informed, data-driven machine-learning methods to capture the fundamental laws of materials thermodynamics and surface kinetics from atomistic simulations and characterization data and empower all group members to construct materials-centric solutions for the most urgent societal challenges, such as climate change, pollution, energy poverty, and food insecurity.
招生信息：
We have multiple openings for self-motivated Ph.D. and Master students to join our research group in Fall 2025! Our group is part of a highly 1point3acres.com with engineering.buffalo.edu at the intersection of materials science and data science. We aim to train graduate students with transdisciplinary skillsets across materials modeling, electrochemistry, catalysis, surface science, data analytics, and machine learning to meet the career demands of an increasingly data-driven and cross-disciplinary world. While we are primarily a computational research group, we are deeply interested in having our students work with experimental collaborators to integrate high-throughput simulations and physics-driven machine learning to facilitate efficient, accurate, and rigorous elucidation of new atomistic insights and physical principles from convoluted experimental characterization data.
Prospective students interested in joining our group are encouraged to directly email Jiayu (1point3acres.com) with an attached CV and transcript and use the email subject line “Prospective Student – [Your Name].” In your email, please also provide a brief summary of your research interests and career goals. We welcome new students from diverse academic backgrounds, including but not limited to materials science, chemistry, physics, chemical engineering, mechanical engineering, and computational and information science. Past academic experience in any of the following subjects is helpful: solid-state physics, physical chemistry, thermodynamics, statistical mechanics, or deep learning, but no prior computational modeling experience is necessary as long as you are highly motivated to learn and apply new skills in our group.
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