Feng Jie, Dong Zhihao, Ji Yujin, Li Youyong
Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China.
JACS Au. 2023 Apr 11;3(4):1131-1140. doi: 10.1021/jacsau.2c00709. eCollection 2023 Apr 24.
The discovery of active and stable catalysts for the oxygen evolution reaction (OER) is vital to improve water electrolysis. To date, rutile iridium dioxide IrO is the only known OER catalyst in the acidic solution, while its poor activity restricts its practical viability. Herein, we propose a universal graph neural network, namely, CrystalGNN, and introduce a dynamic embedding layer to self-update atomic inputs during the training process. Based on this framework, we train a model to accurately predict the formation energies of 10,500 IrO configurations and discover 8 unreported metastable phases, among which 2/-IrO and 62-IrO are identified as excellent electrocatalysts to reach the theoretical OER overpotential limit at their most stable surfaces. Our self-learning-input CrystalGNN framework exhibits reliable accuracy, generalization, and transferring ability and successfully accelerates the bottom-up catalyst design of novel metastable IrO to boost the OER activity.
发现用于析氧反应（OER）的活性和稳定催化剂对于改善水电解至关重要。迄今为止，金红石型二氧化铱IrO是酸性溶液中唯一已知的OER催化剂，但其较差的活性限制了其实际可行性。在此，我们提出了一种通用的图神经网络，即CrystalGNN，并引入了一个动态嵌入层，以便在训练过程中自我更新原子输入。基于此框架，我们训练了一个模型来准确预测10500种IrO构型的形成能，并发现了8个未报道的亚稳相，其中2/-IrO和62-IrO被确定为优异的电催化剂，在其最稳定表面达到理论OER过电位极限。我们的自学习输入CrystalGNN框架表现出可靠的准确性、泛化能力和转移能力，并成功加速了新型亚稳IrO的自下而上的催化剂设计，以提高OER活性。
