Dynamic Reaction Phase Diagrams for Real-Time Catalyst Screening

Guo et al. (2021) introduced reaction phase diagrams (RPDs) for mapping activity/selectivity trends, but these are typically static and offline. Building on recent advances in high-throughput DFT, microkinetic modeling (Worakul et al., 2024), and real-time data visualization, I suggest creating a dynamic RPD tool. This platform would allow researchers to adjust parameters (e.g., temperature, pressure, concentration) and instantly see the predicted effect on catalyst performance and reaction pathways, integrating uncertainty quantification (Jacobs et al., 2024) for robust decision-making. Such a tool bridges the gap between theory and experiment, enabling rapid hypothesis testing and accelerating the iterative catalyst design cycle.

References:

1. Toward computational design of chemical reactions with reaction phase diagram. Chenxi Guo, Xiao-yan Fu, J. Long, Huan Li, Gangqiang Qin, Ang Cao, Huijuan Jing, Jianping Xiao (2021).
2. Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions. Thanapat Worakul, Rubén Laplaza, Shubhajit Das, M. D. Wodrich, Cl´emence Corminboeuf (2024). ACS Catalysis.
3. Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility. R. Jacobs, Lane E. Schultz, Aristana Scourtas, K. Schmidt, Owen Price-Skelly, Will Engler, Ian T. Foster, B. Blaiszik, P. Voyles, Dane Morgan (2024). Machine Learning: Science and Technology.