Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 143, Issue 47, Pages 19769-19777Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.1c08211
Keywords
-
Categories
Funding
- Singapore RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Grant Accelerated Materials Development for Manufacturing by the Agency for Science, Technology and Research [A1898b0043]
- Singapore NRF [R279-000-444-281, R-279-000-483-281]
- National University of Singapore [R279-000-482-133]
Ask authors/readers for more resources
The study demonstrates a self-improving material discovery system for high-performance photosensitizers using accurate prediction and active learning. Through self-improving cycles, the system enhances model prediction accuracy and PS search ability, resulting in the discovery of numerous potential high-performance PSs.
Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet-triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available