Guest Editors
Prof. Dr. Hao Li
Email: li.hao.b8@tohoku.ac.jp
Affiliation: Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan
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Research Interests: catalysis theory; digital catalysis; density functional theory; machine learning; AI for materials
Dr. Heng Liu
Email: heng.liu.e1@tohoku.ac.jp
Affiliation: Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan
Homepage:
Research Interests: Catalysis Design; Digital Catalysis; Density Functional Theory; Machine Learning; Surface state analysis
Summary
This special issue, titled " AI and Machine Learning: Transforming Catalysts Design " delves into the transformative impact of digital technologies on the field of catalysis. As we advance into an era where computational tools and digital methodologies become integral to research and development, the design and optimization of catalysts have benefitted immensely. The integration of these technologies enables researchers to identify potential catalysts, predict catalytic behaviors, design catalysts with precision, and streamline the discovery and development processes, thus significantly advancing the pace of innovation in catalysis.
The aim of this special issue is to highlight the latest advancements and potential of digital tools in catalysis design. It seeks to showcase research that exemplifies the integration of theoretical computations, modeling, data analytics, and digital simulation with traditional catalytic science. The scope extends to theoretical studies that offer deep insights into catalytic mechanisms, practical applications that demonstrate improved catalytic efficiencies, and innovative methodologies that challenge existing paradigms.
Suggested themes for this issue include development of theoretical methods for catalytic processes, database constructions for catalysts and catalysis, machine learning applications in catalyst theory, digital twin technology for catalysis, and virtual screening and design of novel catalysts.
Keywords
Digital catalysis, Theoretical computations & modeling, Machine learning, AI4Science, Database, Catalysis theory
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