bob体官网入口

师资队伍
当前位置: 学院首页 > 师资队伍 > 高分子材料加工工程系 > 副教授-副研究员 > 正文

bob体官网入口:刘晗

  • 职称:特聘副研究员
  • 所属学科:材料加工工程
  • 导师情况:
  • E-mail:happylife@ucla.edu
  • 个人主页:http://www.ppescu.com/xxxx.html

刘晗,bob体官网入口特聘副研究员。本科和硕士均毕业于bob体官网入口,2013年获得高分子材料与工程专业学士学位,2016年获得材料学专业硕士学位,师从黄光速教授,主攻高分子材料结构与性能研究。随后前往美国加州大学洛杉矶分校(UCLA)攻读博士学位,专攻机器学习与计算材料学研究,师从 Mathieu Bauchy教授,2021年获得土木与环境工程专业博士学位,期间获得电子与计算机工程专业硕士双学位(2020年)。博士毕业后随即在UCLA开展博士后工作,与谷歌大脑团队合作,从事图形网络与力学超材料研究工作。2022年加入bob体官网入口,任高分子材料加工工程系特聘副研究员,从事智能计算在高分子加工中的应用研究。

当前其研究方向致力于构建基于人工智能的计算材料平台,实现对材料“制备—结构—性能—应用”的全过程模拟与反向设计,从而精准预测并反向调控材料制备过程,快速降低目标材料的研发周期与成本,达到高性能新材料的加速开发,并在实际应用中促进该平台理论方法学的发展。目前已累计公开发表 SCI 论文25篇,参与编写英文专著1部,其中以第一作者发表15篇 SCI 论文,包括Acta Materialia, ACS Nano, Materials Horizon, Journal of the Mechanics and Physics of Solids, npj Materials Degradation, Applied Physics Letter, Journal of Physics Chemistry B, Journal of Chemical Physics等知名期刊。目前以第一作者在3个不同的材料计算方向撰文发表了3篇SCI 综述,内容受到广泛关注和美国媒体Ceramic Tech Today专题报道。担任npj Materials Degradation,Journal of Cleaner Production, Journal of Applied Physics, Journal of Physics: Condensed Matter等知名期刊特邀审稿人。

教育经历

2016?2021 美国加州大学洛杉矶分校(UCLA),土木与环境工程系,博士

2018?2020 美国加州大学洛杉矶分校(UCLA),电子与计算机工程系,双学位硕士

2013?2016 bob体官网入口,材料学,硕士

2012?2014 四川大学商学院,双学位学士

2010?2013 bob体官网入口,高分子材料与工程,学士

工作经历

2022?至今 bob体官网入口,特聘副研究员

2021?2022 美国加州大学洛杉矶分校(UCLA),博士后研究员

研究领域

机器学习与计算材料学研究

联系方式

通讯地址:四川省成都市一环路南一段24号高分子科学与工程学院,邮编610065

Email:happylife@ucla.edu

个人主页:http://www.ppescu.com/xxxx.html

代表性论文

[1]H. Liu, Z. Zhao, Q. Zhou, R. Chen, K. Yang, Z. Wang, L. Tang, M. Bauchy.Present Challenges and Future Developments in Atomistic Modeling of Glasses: A Review.Comptes Rendus Geoscience2022,doi.org/10.5802/crgeos.116.

[2]H. Liu, S. Xiao, L. Tang, E. Bao, E. Li, C. Yang, Z. Zhao, G. Sant, M. Smedskjaer, L. Guo, M. Bauchy.Predicting the Early-Stage Creep Dynamics of Gels from Their Static Structure by Machine Learning.Acta Materialia2021, 210, 116817.

[3]H. Liu, Y. Liu, Z. Zhao, S. Schoenholz, E. Cubuk, M. Bauchy.End-to-End Differentiability and Tensor Processing Unit Computing to Accelerate Materials’ Inverse Design.Workshop on machine learning for engineering modeling, simulation and design @ NeurIPS2020.

[4]H. Liu, Y. Li, Z. Fu, K. Li, M. Bauchy.Exploring the Landscape of Buckingham Potentials for Silica by Machine Learning: Soft vs Hard Interatomic Forcefields.Journal ofChemical Physics2020, 152, 051101.

[5]H. Liu, Z. Fu, K. Yang, X. Xu, M. Bauchy.Machine Learning for Glass Science and Engineering: A Review.Journal ofNon-Crystalline Solids: X2019, 4, 100036.

[6]H. Liu, T. Zhang, N. Krishnan, M. Smedskjaer, J. Ryan, S. Gin, M. Bauchy.Predicting the Dissolution Kinetics of Silicate Glasses by Topology-informed Machine Learning,npj Materials Degradation2019, 3, 32.

[7]H. Liu, L. Tang, N. Krishnan, G. Sant, M. Bauchy.Structural Percolation Controls the Precipitation Kinetics of Colloidal Calcium–Silicate–Hydrate Gels.Journal of Physics D: Applied Physics2019, 52, 315301.

[8]H. Liu, Z. Fu, Y. Li, N. Sabri, M. Bauchy.Parameterization of Empirical Forcefields for Glassy Silica Using Machine Learning.MRS Communications2019, 9, 593.

[9]H. Liu, S. Dong, L. Tang, N. Krishnan, E. Masoero, G. Sant, M. Bauchy.Long-Term Creep Deformations in Colloidal Calcium–Silicate–Hydrate Gels by Accelerated Aging Simulations.Journal of Colloid and Interface Science2019, 542, 339.

[10] H. Liu, Z. Fu, Y. Li, N. Sabri, M. Bauchy.Balance between Accuracy and Simplicity in Empirical Forcefields for Glass Modeling: Insights from Machine Learning.Journal ofNon-Crystalline Solids2019, 515, 133.

[11] H. Liu, S. Dong, L. Tang, N. Krishnan, G. Sant, M. Bauchy.Effects of Polydispersity and Disorder on the Mechanical Properties of Hydrated Silicate Gels.Journal of the Mechanics and Physics of Solids2019, 122, 555.

[12] H. Liu, T. Du, N. Krishnan, H. Li, M. Bauchy.Topological Optimization of Cementitious Binders: Advances and Challenges.Cement and Concrete Composites2019, 101, 5.

[13] H. Liu, G. Huang, J. Zeng, L. Xu, X. Fu, S. Wu, J. Zheng, J. Wu.ObservingNucleationTransition inStretched Natural Rubber through Self-seeding.Journal of PhysicalChemistry B2015, 119, 11887.

[14] H. Liu, G. Huang, L. Wei, J. Zeng, X. Fu, C. Huang, J. Wu.Inhomogeneous Natural Network Promoting Strain-induced Crystallization: A Mesoscale Model of Natural Rubber.Chinese Journal of Polymer Science2019, 37, 1142.

[15] H. Liu, M. Zhou, Y. Zhou, S. Wang, G. Li, L. Jiang, Y. Dan.AgingLifePredictionSystem ofPolymerOutdoorsConstructed by ANN. 1. LifetimePrediction forPolycarbonate.PolymerDegradation and Stability2014, 105, 218.

[16] S. Xiao,H. Liu, E. Bao, E. Li, C. Yang, Y. Tang, J. Zhou, M. Bauchy.FindingDefects in Disorder: Strain-dependent Structural Fingerprint of Plasticity in Granular Materials.Applied Physics Letters2021, 119, 241904.

[17] Tao. Du,H. Liu, L. Tang, S. S?rensen, M. Bauchy, M. Smedskjaer.Predicting Fracture Propensity in Amorphous Alumina from its Static Structure using Machine Learning.ACS Nano2021, 15, 11, 17705–17716.

[18] L. Tang,H. Liu, G. Ma, T. Du, N. Mousseau, W. Zhou, M. Bauchy.The Energy Landscape Governs Ductility in Disordered Materials.Materials Horizons2021, 8, 1242-1252.

[19] Y. Zhang,H. Liu, Z. Chen, J. W. Ju, M. Bauchy.Deconstructing Water Sorption Isotherms in Cement Pastes by Lattice Density Functional Theory Simulations.Journal of the American Ceramic Society2021, 104, 4226-4238.

[20] R. Christensen, S. S?rensen,H. Liu, K. Li, M. Bauchy, M. Smedskjaer.Interatomic Potential Parameterization Using Particle Swarm Optimization: Case Study of Glassy Silica.Journal ofChemical Physics2021, 154, 134505.

[21] L. Tang, G. Ma,H. Liu, W. Zhou,M. Bauchy.Bulk Metallic Glasses’ Response to Oscillatory Stress Is Governed by the Topography of the Energy Landscape.Journal of Physical Chemistry B2020,124,11294.

[22] C. Zhao, W. Zhou, Q. Zhou, Y. Zhang,H. Liu, G. Sant, X. Liu, L. Guo, M. Bauchy.Precipitation of Calcium–Alumino–Silicate–Hydrate Gels: The Role of the Internal Stress.Journal ofChemical Physics2020, 153, 014501.

[23] L. Xu, C. Huang, M. Luo, W. Qu,H. Liu, Z. Gu, L. Jing, G. Huang, J. Zheng.A Rheological Study on Non-rubber Component Networks in Natural Rubber.RSC Advances2015,5,91742.

[24] C. Huang, G. Huang, S. Li, M. Luo,H. Liu, X. Fu, W. Qu, Z. Xie, J. Wu.Research on Architecture and Composition of Natural Network in Natural Rubber.Polymer2018, 154, 90.

[25] J. Wu, W. Qu, G. Huang, S. Wang, C. Huang,H. Liu.Super-Resolution Fluorescence Imaging of Spatial Organization of Proteins and Lipids in Natural Rubber.Biomacromolecules2017, 18, 1705.

Biography

Han LIU is a materials physicist who is combining computational simulations and machine learning techniques to accelerate materials’ inverse designs for wide-range engineering applications. He obtained his Ph.D. from the Department of Civil and Environmental Engineering at University of California, Los Angeles (UCLA) in 2021, and concurrently obtained a master’s degree from the Department of Electrical and Computer Engineering at UCLA in 2020. Prior to UCLA, he obtained his bachelor’s and master’s degrees from the College of Polymer Science and Engineering at Sichuan University (SCU, CHINA) in 2013 and 2016, respectively. Since 2022, he works as an Associate Professor in the Department of Polymer Processing Engineering at Sichuan University (SCU, CHINA). Prior to the faculty of SCU, he worked as a Postdoctoral Associate in Physics of AmoRphous and Inorganic Solids Laboratory (PARIS Lab) at UCLA.

His researches combine computational simulations and machine learning to accelerate the design of disordered materials, including glassy materials, porous materials, and mechanical metamaterials. So far, he has published 25 scientific papers, including 15 first-author publications. His present research interest lies in the intersection between machine learning and computational materials. By developing the cutting-edge techniques and theories of artificial intelligence (AI), he systematically investigates the AI-computing methodologies for materials modeling and inverse design and, ultimately, aims to build an advanced AI-computing platform for polymer processing (and materials design in general).

Education

2017–2021 Ph.D.: Civil Engineering, University of California, Los Angeles (UCLA)

Department of Civil & Environmental Engineering

2018 – 2020 M.S.: Electrical Engineering, UCLA

Department of Electrical & Computer Engineering

2016 – 2017 Enrolled Ph.D. Student: Materials Science and Engineering, UCLA

Department of Materials Science & Engineering

2013 – 2016 M.S.: Materials Science, Sichuan University

College of Polymer Science and Engineering

2012 – 2014 B.S.: Business Administration, Sichuan University

Business school

2011 – 2013 B.S.: Polymer Materials and Engineering, Sichuan University

College of Polymer Science and Engineering & Polymer Research Institute

2010 – 2011 Enrolled Undergraduate: Chemical Engineering and Technology, Sichuan University

College of Chemical Engineering

●GPA Ranking 1/220, National Scholarship Winner (top 1%)

Work Experience

2022-Present College of Polymer Science and Engineering, Sichuan University

Associate Professor

2021-2022 University of California, Los Angeles (UCLA)

Postdoctoral Associate

Research Interest

Machine learning and computational materials

Contact Information

Address: College of Polymer Science and Engineering, Sichuan University (Wangjiang campus)

No.24 South Section 1, Yihuan Road,

Chengdu, China, 610065

Email:happylife@ucla.edu

Website:http://www.ppescu.com/xxxx.html

Representative Publication

[1]H. Liu, Z. Zhao, Q. Zhou, R. Chen, K. Yang, Z. Wang, L. Tang, M. Bauchy.Present Challenges and Future Developments in Atomistic Modeling of Glasses: A Review.Comptes Rendus Geoscience2022,doi.org/10.5802/crgeos.116.

[2]H. Liu, S. Xiao, L. Tang, E. Bao, E. Li, C. Yang, Z. Zhao, G. Sant, M. Smedskjaer, L. Guo, M. Bauchy.Predicting the Early-Stage Creep Dynamics of Gels from Their Static Structure by Machine Learning.Acta Materialia2021, 210, 116817.

[3]H. Liu, Y. Liu, Z. Zhao, S. Schoenholz, E. Cubuk, M. Bauchy.End-to-End Differentiability and Tensor Processing Unit Computing to Accelerate Materials’ Inverse Design.Workshop on machine learning for engineering modeling, simulation and design @ NeurIPS2020.

[4]H. Liu, Y. Li, Z. Fu, K. Li, M. Bauchy.Exploring the Landscape of Buckingham Potentials for Silica by Machine Learning: Soft vs Hard Interatomic Forcefields.Journal ofChemical Physics2020, 152, 051101.

[5]H. Liu, Z. Fu, K. Yang, X. Xu, M. Bauchy.Machine Learning for Glass Science and Engineering: A Review.Journal ofNon-Crystalline Solids: X2019, 4, 100036.

[6]H. Liu, T. Zhang, N. Krishnan, M. Smedskjaer, J. Ryan, S. Gin, M. Bauchy.Predicting the Dissolution Kinetics of Silicate Glasses by Topology-informed Machine Learning,npj Materials Degradation2019, 3, 32.

[7]H. Liu, L. Tang, N. Krishnan, G. Sant, M. Bauchy.Structural Percolation Controls the Precipitation Kinetics of Colloidal Calcium–Silicate–Hydrate Gels.Journal of Physics D: Applied Physics2019, 52, 315301.

[8]H. Liu, Z. Fu, Y. Li, N. Sabri, M. Bauchy.Parameterization of Empirical Forcefields for Glassy Silica Using Machine Learning.MRS Communications2019, 9, 593.

[9]H. Liu, S. Dong, L. Tang, N. Krishnan, E. Masoero, G. Sant, M. Bauchy.Long-Term Creep Deformations in Colloidal Calcium–Silicate–Hydrate Gels by Accelerated Aging Simulations.Journal of Colloid and Interface Science2019, 542, 339.

[10] H. Liu, Z. Fu, Y. Li, N. Sabri, M. Bauchy.Balance between Accuracy and Simplicity in Empirical Forcefields for Glass Modeling: Insights from Machine Learning.Journal ofNon-Crystalline Solids2019, 515, 133.

[11] H. Liu, S. Dong, L. Tang, N. Krishnan, G. Sant, M. Bauchy.Effects of Polydispersity and Disorder on the Mechanical Properties of Hydrated Silicate Gels.Journal of the Mechanics and Physics of Solids2019, 122, 555.

[12] H. Liu, T. Du, N. Krishnan, H. Li, M. Bauchy.Topological Optimization of Cementitious Binders: Advances and Challenges.Cement and Concrete Composites2019, 101, 5.

[13] H. Liu, G. Huang, J. Zeng, L. Xu, X. Fu, S. Wu, J. Zheng, J. Wu.ObservingNucleationTransition inStretched Natural Rubber through Self-seeding.Journal of PhysicalChemistry B2015, 119, 11887.

[14] H. Liu, G. Huang, L. Wei, J. Zeng, X. Fu, C. Huang, J. Wu.Inhomogeneous Natural Network Promoting Strain-induced Crystallization: A Mesoscale Model of Natural Rubber.Chinese Journal of Polymer Science2019, 37, 1142.

[15] H. Liu, M. Zhou, Y. Zhou, S. Wang, G. Li, L. Jiang, Y. Dan.AgingLifePredictionSystem ofPolymerOutdoorsConstructed by ANN. 1. LifetimePrediction forPolycarbonate.PolymerDegradation and Stability2014, 105, 218.

[16] S. Xiao,H. Liu, E. Bao, E. Li, C. Yang, Y. Tang, J. Zhou, M. Bauchy.FindingDefects in Disorder: Strain-dependent Structural Fingerprint of Plasticity in Granular Materials.Applied Physics Letters2021, 119, 241904.

[17] Tao. Du,H. Liu, L. Tang, S. S?rensen, M. Bauchy, M. Smedskjaer.Predicting Fracture Propensity in Amorphous Alumina from its Static Structure using Machine Learning.ACS Nano2021, 15, 11, 17705–17716.

[18] L. Tang,H. Liu, G. Ma, T. Du, N. Mousseau, W. Zhou, M. Bauchy.The Energy Landscape Governs Ductility in Disordered Materials.Materials Horizons2021, 8, 1242-1252.

[19] Y. Zhang,H. Liu, Z. Chen, J. W. Ju, M. Bauchy.Deconstructing Water Sorption Isotherms in Cement Pastes by Lattice Density Functional Theory Simulations.Journal of the American Ceramic Society2021, 104, 4226-4238.

[20] R. Christensen, S. S?rensen,H. Liu, K. Li, M. Bauchy, M. Smedskjaer.Interatomic Potential Parameterization Using Particle Swarm Optimization: Case Study of Glassy Silica.Journal ofChemical Physics2021, 154, 134505.

[21] L. Tang, G. Ma,H. Liu, W. Zhou,M. Bauchy.Bulk Metallic Glasses’ Response to Oscillatory Stress Is Governed by the Topography of the Energy Landscape.Journal of Physical Chemistry B2020,124,11294.

[22] C. Zhao, W. Zhou, Q. Zhou, Y. Zhang,H. Liu, G. Sant, X. Liu, L. Guo, M. Bauchy.Precipitation of Calcium–Alumino–Silicate–Hydrate Gels: The Role of the Internal Stress.Journal ofChemical Physics2020, 153, 014501.

[23] L. Xu, C. Huang, M. Luo, W. Qu,H. Liu, Z. Gu, L. Jing, G. Huang, J. Zheng.A Rheological Study on Non-rubber Component Networks in Natural Rubber.RSC Advances2015,5,91742.

[24] C. Huang, G. Huang, S. Li, M. Luo,H. Liu, X. Fu, W. Qu, Z. Xie, J. Wu.Research on Architecture and Composition of Natural Network in Natural Rubber.Polymer2018, 154, 90.

[25] J. Wu, W. Qu, G. Huang, S. Wang, C. Huang,H. Liu.Super-Resolution Fluorescence Imaging of Spatial Organization of Proteins and Lipids in Natural Rubber.Biomacromolecules2017, 18, 1705.

关闭

书记信箱 院长信箱
bob体官网入口(南极)有限公司官网登录