Pandit Neeraj Kumar, Roy Diptendu, Mandal Shyama Charan, Pathak Biswarup
Department of Chemistry, Indian Institute of Technology Indore, Indore 453552, India.
J Phys Chem Lett. 2022 Aug 18;13(32):7583-7593. doi: 10.1021/acs.jpclett.2c01401. Epub 2022 Aug 11.
Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate's surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.
具有成本效益的电催化剂可替代用于析氢反应（HER）的基于贵金属铂族金属（PGM）的催化剂，这对于可持续能源解决方案具有巨大潜力。近年来，机器学习（ML）方法为智能筛选和预测高效多相催化剂提供了新途径。我们将密度泛函理论（DFT）和监督式ML方法结合起来，以发现地球上储量丰富的活性多相NiCoCu基HER催化剂。设计了一个直观的广义微观结构模型来研究吸附物的表面覆盖率，并为ML过程生成输入特征。该研究利用优化的极端梯度提升回归（XGBR）模型来筛选用于HER的NiCoCu合金基催化剂。我们表明，通过这种方法可以从大量催化剂中筛选出最具活性的HER催化剂。因此，我们的方法可以为发现用于各种电化学反应的新型多相催化剂提供一种有效途径。
