Data Science for Catalysis
The intersection of data science and catalysis is unlocking new frontiers in both fundamental research and industrial applications. We are excited to announce a Special Issue in the Journal of Chemical Physics (JCP) dedicated to exploring these groundbreaking developments under the theme “Data Science for Catalysis”.As we enter the era of data-driven science, the integration of quantum chemistry and machine learning algorithms has become a cornerstone for advancing our understanding of catalytic processes. This Special Issue seeks to highlight the significant impact of catalysis informatics, where data-enabled physical modeling and materials discovery are revolutionizing how we approach catalytic design and optimization.
This Special Issue aims to provide a comprehensive overview of how data science is reshaping the landscape of catalysis. We encourage submissions that offer novel insights, practical solutions, and pioneering approaches to harnessing data for scientific and technological advancements.
Topics covered include, but are not limited to:
- Foundation Models: Making available advanced machine learning frameworks to the general scientific community.
- Automation & Robotics: Innovations in experiment automation and robotic systems that are accelerating data collection and analysis.
- Optimization Techniques: New methodologies for optimizing catalytic processes and materials.
- Data Mining & Curation: Effective strategies for extracting insights from vast datasets and ensuring data quality.
- Data Acquisition & Sharing: Best practices for acquiring high-quality data and facilitating open data sharing.
- Data Infrastructure: Building robust infrastructures to support data-driven research in catalysis.
Guest Editors
Hongliang Xin, Virginia Tech
Nong Artrith, Utrecht University