ABSTRACT
Artificial intelligence (AI) based self-learning or self-improving material discovery systems will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principles calculation 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, which is one of the typical examples of how AI can be used to accelerate materials innovation and facilitate science development in general.
Index Terms
- AI for Materials Innovation: Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principles Simulation
Recommendations
Electronic transport properties and CO adsorption characteristics on TiO2 molecular device - A first-principles study
The transport and CO adsorption properties on rutile TiO2 molecular device is studied using DFT method. The transport characteristics of TiO2 nanostructure are described in terms of electron density, density of states and transmission spectrum. The ...
First-principles study of electron transport in azulene molecular junction: effect of electrode material on electrical rectification behavior
The feasibility of using an azulene molecule as a molecular rectifier with different electrode materials, viz. gold (Au), silver (Ag), and copper (Cu), was investigated using density functional theory (DFT) and the nonequilibrium Green's function (NEGF) ...
Comments