Catalyst design via descriptors

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Toward computational design of chemical reactions with reaction phase diagram - Guo - 2021 - WIREs Computational Molecular Science - Wiley Online Library

Zhijian ZHAO, Professor, Ph.D., Tianjin University, Tianjin, tju, School of Chemical Engineering and Technology

Publications-Energy & Catalysis Adventure Team @ TJU

Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

Rational design of catalysts with earth‐abundant elements - Xu - 2023 - WIREs Computational Molecular Science - Wiley Online Library

Modeling with DFT and Chemical Descriptors Approach for the Development of Catalytic Alloys for PEMFCs

Polarizable Additive with Intermediate Chelation Strength for Stable Aqueous Zinc-Ion Batteries

Catalyst design via descriptors

Addressing complexity in catalyst design: From volcanos and

Accelerating the evaluation of crucial descriptors for catalyst screening via message passing neural network - Digital Discovery (RSC Publishing)

Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors