Actively learned machine with non-ab initio input features toward eficient $CO_2$ reduction catalyst능동적 기계 학습과 비(非)제일원리 표현자를 활용한 효율적인 이산화탄소 환원 촉매 설계에 대한 이론적 연구
In conventional chemisorption model, the d-band center theory plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory plus electronegativity as a simple set of alternative descriptors for chemisorption, which do not demand the ab initio calculations. This pair of descriptors are then combined with machine learning methods, namely, artificial neural network (ANN) and kernel ridge regression (KRR), to allow large-scale materials screenings. We show, for a toy set of 263 alloy systems, that the CO adsorption energy can be predicted with a remarkably small mean absolute deviation error of 0.05 eV, a significantly improved result as compared to 0.13 eV obtained with descriptors including costly d-band center calculations in literature. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV otherwise. As a practical application of this machine, we identified Cu3Y@Cu as a highly active and cost-effective electrochemical CO_2 reduction catalyst to produce CO with the overpotential 0.37 V lower than Au catalyst.