About Prof. Hao Li
Jr. Principal Investigator
Associate Professor Advanced Institute for Materials Research (WPI-AIMR) Tohoku University, Japan Associate Investigator ARC Centre of Excellence for Green Electrochemical Transformation of Carbon Dioxide (GETCO2) The University of Queensland, Australia Tel: +81-022-217-6371 (lab) Email: [email protected] |
Postdoctoral Researcher (2020-2022.03)
Center for Catalysis Theory - Department of Physics Technical University of Denmark (DTU), Denmark Supervisor: Prof. Jens K. Nørskov (Member of the US National Academy of Engineering) Ph.D. Chemistry (2015-2019) Department of Chemistry The Oden Institute for Computational and Engineering Sciences The University of Texas at Austin (UT Austin), USA Supervisor: Prof. Graeme Henkelman Visiting Research Fellow (Fall 2017) Institute for Pure & Applied Mathematics (IPAM) University of California, Los Angeles (UCLA), USA B.S. Chemistry (2011-2015) Sichuan University (SCU), China |
Honors and Awards
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Sep 2024);
• Outstanding Reviewer, Chemical Communications, by Royal Society of Chemistry (Mar 2024);
• Excellent Young Editorial Board Member, Nano Materials Science, by Elsevier (Mar 2024);
• Core Research Cluster for Materials Science Award 2023, Tohoku University (Nov 2023);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2023);
• Most popular article in 2022 for Journal of Materials Chemistry A, by Royal Society of Chemistry (Jan 2023);
• Outstanding Review-Type Paper of Chemistry – A European Journal, by Wiley Publisher (Dec 2022);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2022);
• Travel Award of Energies and Applied Sciences (Switzerland) (April 2022);
• Surface Science Young Investigator, by American Chemical Society (ACS) (Mar 2022);
• Most popular article in 2021 for Journal of Materials Chemistry A, by Royal Society of Chemistry (Jan 2022);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2021);
• AIChE Best Fundamental Paper Award, by American Institute of Chemical Engineers (AIChE) (Oct 2020);
• 2019 Top 10 Outstanding Reviewer of Chemical Communications, by Royal Society of Chemistry (Mar 2020);
• 2019 University Graduate Continuing Fellowship (Highest Fellowship in UT Graduate School) (2019);
• 2019 Emerging Investigators in Materials Chemistry, by Royal Society of Chemistry (May 2019);
• Crowd Favorite Research, UT Energy Week Research Showcase (Feb 2019);
• 2019 MDPI Sustainability Travel Award (Switzerland) (Feb 2019);
• Graduate Student Professional Development Award, UT Austin (Feb 2019);
• Department Excellence Fellowship (Highest Fellowship in UT Chem) (Jan 2019);
• Finalist, ECR Reviewers' Choice Award; Selected as the Top 6 scientific reviewers from ~600 worldwide candidates (by Publons) (Sep 2019);
• 2018 Top (1%) Journal Reviewers in Chemistry (by Publons) (Sep 2018);
• 2017 Top (1%) Journal Reviewers in Materials Science (by Publons) (Sep 2017);
• 2017 Hamilton/Schoch Fellowship, UT Austin (Jan 2017);
• Most Valued Reviewers of Infrared Physics and Technology, Elsevier (Apr 2017);
• “Undergraduate Academic Star of Sichuan University”, SCU (May 2014);
• First Class Undergraduate Fellowship, SCU (Apr 2017);
• Gold Prize, for the 12th Sichuan Undergraduate Academic Science and Technology, China (Jan 2013);
• Shimadzu Fellowship, Shimadzu Corp. & SCU (Jun 2012);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Sep 2024);
• Outstanding Reviewer, Chemical Communications, by Royal Society of Chemistry (Mar 2024);
• Excellent Young Editorial Board Member, Nano Materials Science, by Elsevier (Mar 2024);
• Core Research Cluster for Materials Science Award 2023, Tohoku University (Nov 2023);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2023);
• Most popular article in 2022 for Journal of Materials Chemistry A, by Royal Society of Chemistry (Jan 2023);
• Outstanding Review-Type Paper of Chemistry – A European Journal, by Wiley Publisher (Dec 2022);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2022);
• Travel Award of Energies and Applied Sciences (Switzerland) (April 2022);
• Surface Science Young Investigator, by American Chemical Society (ACS) (Mar 2022);
• Most popular article in 2021 for Journal of Materials Chemistry A, by Royal Society of Chemistry (Jan 2022);
• Top 2% of the World’s Most Highly Cited Scientists, by Stanford University and Elsevier publisher (Oct 2021);
• AIChE Best Fundamental Paper Award, by American Institute of Chemical Engineers (AIChE) (Oct 2020);
• 2019 Top 10 Outstanding Reviewer of Chemical Communications, by Royal Society of Chemistry (Mar 2020);
• 2019 University Graduate Continuing Fellowship (Highest Fellowship in UT Graduate School) (2019);
• 2019 Emerging Investigators in Materials Chemistry, by Royal Society of Chemistry (May 2019);
• Crowd Favorite Research, UT Energy Week Research Showcase (Feb 2019);
• 2019 MDPI Sustainability Travel Award (Switzerland) (Feb 2019);
• Graduate Student Professional Development Award, UT Austin (Feb 2019);
• Department Excellence Fellowship (Highest Fellowship in UT Chem) (Jan 2019);
• Finalist, ECR Reviewers' Choice Award; Selected as the Top 6 scientific reviewers from ~600 worldwide candidates (by Publons) (Sep 2019);
• 2018 Top (1%) Journal Reviewers in Chemistry (by Publons) (Sep 2018);
• 2017 Top (1%) Journal Reviewers in Materials Science (by Publons) (Sep 2017);
• 2017 Hamilton/Schoch Fellowship, UT Austin (Jan 2017);
• Most Valued Reviewers of Infrared Physics and Technology, Elsevier (Apr 2017);
• “Undergraduate Academic Star of Sichuan University”, SCU (May 2014);
• First Class Undergraduate Fellowship, SCU (Apr 2017);
• Gold Prize, for the 12th Sichuan Undergraduate Academic Science and Technology, China (Jan 2013);
• Shimadzu Fellowship, Shimadzu Corp. & SCU (Jun 2012);
Editorial & Reviewer Service
• Associate Editor, Journal of Materials Informatics, 2023-Present;
• Associate Editor, Frontiers in Catalysis, 2022-Present;
• Member of Editorial Board, CMC-Computers, Materials & Continua, 2024-Present;
• Member of Editorial Board, AIMS Materials Science, AIMS Press, 2024-Present;
• Editorial Board Member, Energies, 2021-Present;
• Editorial Board Member, Symmetry, 2024-Present;
• Invited Lead Guest Editor, Nano Materials Science (Elsevier), 2019-Present;
• Invited Lead Guest Editor, Applied Sciences, 2021-Present;
• Lead Guest Editor, International Journal of Photoenergy, 2020-2022;
• Guest Editor, Frontiers of Chemical Science and Engineering, 2023-Present; Materials, 2020;
• Reviewer: regularly review manuscripts for journals including Nature, Nature series journals, Sci. Adv., Adv. Mater., Angew. Chem. Int. Ed., Chem. Sci., Adv. Funct. Mater., ACS Catal., ACS Energy Lett., J. Mater. Chem. A & C, J. Chem. Phys., J. Phys. Chem. & Lett., Langmuir, Chem. Common., Phys. Chem. Chem. Phys., Small, Dalton Trans., ChemSusChem, J. Appl. Phys., Adv. Sci., ChemCatChem, Catal. Sci. Technol., Catal. Rev., ChemPhysChem, Nanoscale, Environ. Sci. Technol, Adv. Theory Simul., Sol. Energy, J. Alloy Compd., Comp. Mater. Sci., Sci. Total Environ., J. CO2 Util., Appl. Soft Comput., Energy Storage Materials, J. Theor. Comput. Chem., etc.
• Associate Editor, Journal of Materials Informatics, 2023-Present;
• Associate Editor, Frontiers in Catalysis, 2022-Present;
• Member of Editorial Board, CMC-Computers, Materials & Continua, 2024-Present;
• Member of Editorial Board, AIMS Materials Science, AIMS Press, 2024-Present;
• Editorial Board Member, Energies, 2021-Present;
• Editorial Board Member, Symmetry, 2024-Present;
• Invited Lead Guest Editor, Nano Materials Science (Elsevier), 2019-Present;
• Invited Lead Guest Editor, Applied Sciences, 2021-Present;
• Lead Guest Editor, International Journal of Photoenergy, 2020-2022;
• Guest Editor, Frontiers of Chemical Science and Engineering, 2023-Present; Materials, 2020;
• Reviewer: regularly review manuscripts for journals including Nature, Nature series journals, Sci. Adv., Adv. Mater., Angew. Chem. Int. Ed., Chem. Sci., Adv. Funct. Mater., ACS Catal., ACS Energy Lett., J. Mater. Chem. A & C, J. Chem. Phys., J. Phys. Chem. & Lett., Langmuir, Chem. Common., Phys. Chem. Chem. Phys., Small, Dalton Trans., ChemSusChem, J. Appl. Phys., Adv. Sci., ChemCatChem, Catal. Sci. Technol., Catal. Rev., ChemPhysChem, Nanoscale, Environ. Sci. Technol, Adv. Theory Simul., Sol. Energy, J. Alloy Compd., Comp. Mater. Sci., Sci. Total Environ., J. CO2 Util., Appl. Soft Comput., Energy Storage Materials, J. Theor. Comput. Chem., etc.
Notable Talks (Invited)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of the Oak Ridge National Laboratory, USA (November 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Vanderbilt University, USA (November 2024)
• H. Li, “Current Progress of “Turing Plan” for AI-Driven Materials Design”, Seminar of Michigan State University, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of The State University of New York at Buffalo, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Illinois Institute of Technology, Illinois, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Purdue University, Indiana, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, AIMED HetCat Workshop, Chicago, USA (October 2024)
• H. Li, “Catalysis Theory Designs Good Catalysts”, FRIS Symposium, Tohoku University (August 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Zhejiang A&F University, Hangzhou, China (July 2024)
• H. Li, “Development of the DigCat Platform”, Seminar of Zhejiang University (2), Hangzhou, China (July 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Zhejiang University (1), Hangzhou, China (July 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Nanjing Normal University, Nanjing, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of North China Electric Power University, Baoding, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Tianjin University, Tianjin, China (June 2024)
• H. Li, “An AI-Driven “Theory-Methodology-Experiment” Framework for Catalyst Design”, The 34th CCS Congress, Guangzhou, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of SUSTech, Shenzhen, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Songshan Lake Materials Laboratory, Dongguan, China (June 2024)
• H. Li, “AI- and Theory-Driven Development of High-Performance Catalysts”, AIMR-SUSTech Joint Workshop, Shenzhen, China (June 2024)
• H. Li, “AI + Data-Driven Methods for Functional Materials Design”, Seminar of Tsinghua University, Beijing, China (May 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis”, 8th Asia-Pacific Conference on Ionic Liquids and Green Processes (APCIL-8), Henan, China (May 2024)
• H. Li, “Combining Data Science and AI for Materials Design”, Seminar of Dalian Institute of Chemical Physics, Dalian, China (May 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis”, 20th National Youth Catalysis Academic Conference (NYCC20) of the Chinese Chemical Society, Dalian, China (May 2024)
• H. Li, “Combining Data Science and AI for Materials Design”, Seminar of Dalian University of Technology, Dalian, China (May 2024)
• H. Li, “Data-Driven Design of Functional Materials”, Seminar of Beijing University of Chemical Technology, Beijing, China (May 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of University of California San Diego, USA (April 2024)
• H. Li, “What Can Theory Do For Us?”, Seminar of University of Science and Technology of China (April 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis” Seminar of Chinese Chemical Society, Hefei, China (April 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of NIMS, Japan (March 2024)
• H. Li, “Design of Catalysts by a Data-Driven Framework”, Seminar of University of California, Los Angeles (UCLA), USA (March 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of Wayne State University, USA (Feb 2024)
• H. Li, “Fusing Theory and Experiments to Realize Materials Design”, 11th Early Career Researchers Ensemble Workshop, Tohoku University, Japan (December 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Design Effective Materials for Hydrogen Generation and Utilization”, Symposiumof MRM, Kyoto Japan (December 2023)
• H. Li, “Do's and Don'ts in Computational Catalysis”, Seminar of The University of Sydney, Australia (December 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of The University of Adelaide, Australia (December 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, ISGTCO2 International Symposium, Australia (December 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, CRCMS International Symposium (November 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, International Conference on Carbon Resources to Chemicals, Taiyuan, China (November 2023)
• H. Li, “Searching for Low-Cost and Stable Transition Metal X-ide Materials for Electrocatalytic Hydrogen Generation and Utilization”, Tsinghua-Tohoku Workshop (October 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, Meeting with the Delegation of Science and Technology from the Embassy of France (September 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Summer Workshop on AI for Materials, Chongqing, China (July 2023)
• H. Li, “The Rational Design and Understanding of CO2 Reduction Catalysts”, Seminar of Taiyuan University of Technology (July 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework for Catalyst Design”, Seminar of Tsinghua University (June 2023)
• H. Li, “Catalyst Engineering for A Sustainable Future”, Seminar of North China Electricity Power University (June 2023)
• H. Li, “Data-driven Design of Effective Catalysts”, Seminar of Yanshan University (June 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework for Effective Catalyst Design”, Seminar of University of Science and Technology of China (May 2023)
• H. Li, “Exploring the Catalysis Universe”, Seminar of Hefei University of Technology (May 2023)
• H. Li, “Understanding Experimental Observations based on Catalysis Theory”, Seminar of East China University of Science and Technology (May 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, University of Cambridge - AIMR Joint Workshop (April 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Imperial College London - AIMR Joint Workshop (April 2023)
• H. Li, “Is Machine Learning the Only Way-out of Computational Materials?”, 2nd International Conference on Data Driven Materials Innovation and Carbon Neutrality (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, 2nd International Conference on Data Driven Materials Innovation and Carbon Neutrality (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Joint Seminar of Aarhus University, Purdue University, and Tohoku University (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Seminar of the Department of Chemical Engineering, The University of Manchester (January 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Seminar of the School of Chemical Engineering, The University of Sydney (December 2022)
• H. Li, “Design High-Performance Catalysts by a Data-Driven Framework”, Seminar of the School of Chemical Engineering, Beijing University of Chemical Technology (November 2022)
• H. Li, “The Role of Catalysis Theory and Data Science for a Sustainable Future”, Seminar of the Australian National University (October 2022)
• H. Li, “A Data-Driven Framework for Effective Catalyst Design”, Online Seminar Sichuan University (September 2022)
• H. Li, “Design of Electrocatalysts by Materials Theory and Machine Learning”, Annual Meeting of the Chemical Society of Japan, Morioka, Japan (September 2022)
• H. Li, “Design of Materials by Theory”, 33rd IUPAP Conference on Computational Physics, University of Texas at Austin (August 2022)
• H. Li, “How to Precisely Design Catalysts by Materials Theory and Data-Science”, Online Seminar of Harbin Institute of Technology (August 2022)
• H. Li, “Introduction to a Collaborative Materials Design Framework of Hao Li Lab”, Tohoku University – University of Melbourne Joint Workshop (June 2022)
• H. Li, “Design of Catalysts Realized by Materials Theory and Machine Learning”, The University of Cambridge – AIMR Workshop (April 2022)
• H. Li, “Design of CO2 Reduction Electrocatalysts Using Materials Theory and Machine Learning”, Invited online seminar of Qingdao University (April 2022)
• H. Li, “Exploring the “Catalysis Universe” from Data and Theory”, AIMR Tea-Time Talk (April 2022)
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", ACS National Meeting, San Diego CA (Mar 2022) (as "Surface Science Young Investigator");
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of School of Chemical Engineering, The University of Queensland, Australia (Jan 2022);
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of Department of Chemical Engineering, Polytechnique Montréal, Canada (Oct 2021);
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of School of Energy and Environment, City University of Hong Kong, HK (Sep 2021);
• H. Li, " Reducing the Complexity in Catalyst Design by Mathematical Modeling", Seminar of School of Mathematics, UCT Prague, Czech Republic (Jul 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of Department of Chemistry, Illinois Institute of Technology, Chicago IL (Jun 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of NCCR Catalysis, ETH Zürich, Switzerland (Apr 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of Chemistry, University of Akron, Akron OH (Apr 2021);
• H. Li, "Combining Theory, Methodology, and Experiments for the Design of Catalytic Materials" Seminar of Catalysis Research Center, Technical University of Munich, Germany (Apr 2021);
• H. Li, "Modeling of Catalytic Reactions at Alloy Surfaces", Seminar of School of Energy, North China Electric Power University (Jun 2020);
• H. Li, "Unifying Theory, Modeling, and Experiments in Chemistry", Chemistry Seminar in Princeton University, Princeton NJ (Sep 2019);
• H. Li, "TensorFlow- and Keras-Based Machine Learning Frameworks for Fitting Potential Energy Surfaces: A Collaborative Project Originated in IPAM", Reunion Conference for EL2017, IPAM CA (Jun 2019);
• H. Li, "New Design Strategy for Effective Alloy Catalysts", CCE-2019, Houston TX (Feb 2019) (MDPI Travel Award);
• H. Li, "Catalytic Reactions at Alloy Surfaces", Chemical Reactions at Surfaces, Gordon Research Seminar, Ventura CA (Feb 2019) (UT Graduate School Travel Award);
• H. Li, "How to Train a Precise Neural Network for Atomistic Simulation", UT Summer Semester (Jun 2018);
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of the Oak Ridge National Laboratory, USA (November 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Vanderbilt University, USA (November 2024)
• H. Li, “Current Progress of “Turing Plan” for AI-Driven Materials Design”, Seminar of Michigan State University, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of The State University of New York at Buffalo, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Illinois Institute of Technology, Illinois, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, Seminar of Purdue University, Indiana, USA (October 2024)
• H. Li, “Turing Scheme for Catalysis and DigCat 3.0 – An Intelligent Digital Platform Powered by Ultra-Large-Scale Exp + Comput Data”, AIMED HetCat Workshop, Chicago, USA (October 2024)
• H. Li, “Catalysis Theory Designs Good Catalysts”, FRIS Symposium, Tohoku University (August 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Zhejiang A&F University, Hangzhou, China (July 2024)
• H. Li, “Development of the DigCat Platform”, Seminar of Zhejiang University (2), Hangzhou, China (July 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Zhejiang University (1), Hangzhou, China (July 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Nanjing Normal University, Nanjing, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of North China Electric Power University, Baoding, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Tianjin University, Tianjin, China (June 2024)
• H. Li, “An AI-Driven “Theory-Methodology-Experiment” Framework for Catalyst Design”, The 34th CCS Congress, Guangzhou, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of SUSTech, Shenzhen, China (June 2024)
• H. Li, “AI- and Theory-Driven Design of High-Performance Materials”, Seminar of Songshan Lake Materials Laboratory, Dongguan, China (June 2024)
• H. Li, “AI- and Theory-Driven Development of High-Performance Catalysts”, AIMR-SUSTech Joint Workshop, Shenzhen, China (June 2024)
• H. Li, “AI + Data-Driven Methods for Functional Materials Design”, Seminar of Tsinghua University, Beijing, China (May 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis”, 8th Asia-Pacific Conference on Ionic Liquids and Green Processes (APCIL-8), Henan, China (May 2024)
• H. Li, “Combining Data Science and AI for Materials Design”, Seminar of Dalian Institute of Chemical Physics, Dalian, China (May 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis”, 20th National Youth Catalysis Academic Conference (NYCC20) of the Chinese Chemical Society, Dalian, China (May 2024)
• H. Li, “Combining Data Science and AI for Materials Design”, Seminar of Dalian University of Technology, Dalian, China (May 2024)
• H. Li, “Data-Driven Design of Functional Materials”, Seminar of Beijing University of Chemical Technology, Beijing, China (May 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of University of California San Diego, USA (April 2024)
• H. Li, “What Can Theory Do For Us?”, Seminar of University of Science and Technology of China (April 2024)
• H. Li, “Turing Scheme of Catalysis: Development of the Front-End of AI Lab for Electrocatalysis” Seminar of Chinese Chemical Society, Hefei, China (April 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of NIMS, Japan (March 2024)
• H. Li, “Design of Catalysts by a Data-Driven Framework”, Seminar of University of California, Los Angeles (UCLA), USA (March 2024)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of Wayne State University, USA (Feb 2024)
• H. Li, “Fusing Theory and Experiments to Realize Materials Design”, 11th Early Career Researchers Ensemble Workshop, Tohoku University, Japan (December 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Design Effective Materials for Hydrogen Generation and Utilization”, Symposiumof MRM, Kyoto Japan (December 2023)
• H. Li, “Do's and Don'ts in Computational Catalysis”, Seminar of The University of Sydney, Australia (December 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Seminar of The University of Adelaide, Australia (December 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, ISGTCO2 International Symposium, Australia (December 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, CRCMS International Symposium (November 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, International Conference on Carbon Resources to Chemicals, Taiyuan, China (November 2023)
• H. Li, “Searching for Low-Cost and Stable Transition Metal X-ide Materials for Electrocatalytic Hydrogen Generation and Utilization”, Tsinghua-Tohoku Workshop (October 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, Meeting with the Delegation of Science and Technology from the Embassy of France (September 2023)
• H. Li, “The Cat-Universe: A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Summer Workshop on AI for Materials, Chongqing, China (July 2023)
• H. Li, “The Rational Design and Understanding of CO2 Reduction Catalysts”, Seminar of Taiyuan University of Technology (July 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework for Catalyst Design”, Seminar of Tsinghua University (June 2023)
• H. Li, “Catalyst Engineering for A Sustainable Future”, Seminar of North China Electricity Power University (June 2023)
• H. Li, “Data-driven Design of Effective Catalysts”, Seminar of Yanshan University (June 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework for Effective Catalyst Design”, Seminar of University of Science and Technology of China (May 2023)
• H. Li, “Exploring the Catalysis Universe”, Seminar of Hefei University of Technology (May 2023)
• H. Li, “Understanding Experimental Observations based on Catalysis Theory”, Seminar of East China University of Science and Technology (May 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Materials Design”, University of Cambridge - AIMR Joint Workshop (April 2023)
• H. Li, “A “Data-Theory-Methodology-Experiment” Framework to Realize Catalyst Design”, Imperial College London - AIMR Joint Workshop (April 2023)
• H. Li, “Is Machine Learning the Only Way-out of Computational Materials?”, 2nd International Conference on Data Driven Materials Innovation and Carbon Neutrality (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, 2nd International Conference on Data Driven Materials Innovation and Carbon Neutrality (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Joint Seminar of Aarhus University, Purdue University, and Tohoku University (February 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Seminar of the Department of Chemical Engineering, The University of Manchester (January 2023)
• H. Li, “The Cat-Universe: Design of Catalysts by A Data-Driven Framework”, Seminar of the School of Chemical Engineering, The University of Sydney (December 2022)
• H. Li, “Design High-Performance Catalysts by a Data-Driven Framework”, Seminar of the School of Chemical Engineering, Beijing University of Chemical Technology (November 2022)
• H. Li, “The Role of Catalysis Theory and Data Science for a Sustainable Future”, Seminar of the Australian National University (October 2022)
• H. Li, “A Data-Driven Framework for Effective Catalyst Design”, Online Seminar Sichuan University (September 2022)
• H. Li, “Design of Electrocatalysts by Materials Theory and Machine Learning”, Annual Meeting of the Chemical Society of Japan, Morioka, Japan (September 2022)
• H. Li, “Design of Materials by Theory”, 33rd IUPAP Conference on Computational Physics, University of Texas at Austin (August 2022)
• H. Li, “How to Precisely Design Catalysts by Materials Theory and Data-Science”, Online Seminar of Harbin Institute of Technology (August 2022)
• H. Li, “Introduction to a Collaborative Materials Design Framework of Hao Li Lab”, Tohoku University – University of Melbourne Joint Workshop (June 2022)
• H. Li, “Design of Catalysts Realized by Materials Theory and Machine Learning”, The University of Cambridge – AIMR Workshop (April 2022)
• H. Li, “Design of CO2 Reduction Electrocatalysts Using Materials Theory and Machine Learning”, Invited online seminar of Qingdao University (April 2022)
• H. Li, “Exploring the “Catalysis Universe” from Data and Theory”, AIMR Tea-Time Talk (April 2022)
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", ACS National Meeting, San Diego CA (Mar 2022) (as "Surface Science Young Investigator");
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of School of Chemical Engineering, The University of Queensland, Australia (Jan 2022);
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of Department of Chemical Engineering, Polytechnique Montréal, Canada (Oct 2021);
• H. Li, "Design of Catalysts Realized by Materials Theory and Machine Learning", Seminar of School of Energy and Environment, City University of Hong Kong, HK (Sep 2021);
• H. Li, " Reducing the Complexity in Catalyst Design by Mathematical Modeling", Seminar of School of Mathematics, UCT Prague, Czech Republic (Jul 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of Department of Chemistry, Illinois Institute of Technology, Chicago IL (Jun 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of NCCR Catalysis, ETH Zürich, Switzerland (Apr 2021);
• H. Li, "Reducing the Complexity in Catalyst Design", Seminar of Chemistry, University of Akron, Akron OH (Apr 2021);
• H. Li, "Combining Theory, Methodology, and Experiments for the Design of Catalytic Materials" Seminar of Catalysis Research Center, Technical University of Munich, Germany (Apr 2021);
• H. Li, "Modeling of Catalytic Reactions at Alloy Surfaces", Seminar of School of Energy, North China Electric Power University (Jun 2020);
• H. Li, "Unifying Theory, Modeling, and Experiments in Chemistry", Chemistry Seminar in Princeton University, Princeton NJ (Sep 2019);
• H. Li, "TensorFlow- and Keras-Based Machine Learning Frameworks for Fitting Potential Energy Surfaces: A Collaborative Project Originated in IPAM", Reunion Conference for EL2017, IPAM CA (Jun 2019);
• H. Li, "New Design Strategy for Effective Alloy Catalysts", CCE-2019, Houston TX (Feb 2019) (MDPI Travel Award);
• H. Li, "Catalytic Reactions at Alloy Surfaces", Chemical Reactions at Surfaces, Gordon Research Seminar, Ventura CA (Feb 2019) (UT Graduate School Travel Award);
• H. Li, "How to Train a Precise Neural Network for Atomistic Simulation", UT Summer Semester (Jun 2018);