From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

X Fan, L Chen, D Huang, Y Tian… - Advanced Functional …, 2024 - Wiley Online Library
The rapid advancement of high‐performance computing and artificial intelligence
technology has opened up novel avenues for the development of various metal …

[HTML][HTML] Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

X Liu, HJ Peng - Engineering, 2024 - Elsevier
Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and
energy conversion processes, and its revolution necessitates the hunt for new materials with …

A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry

M Tamtaji, S Chen, Z Hu, WA Goddard III… - The Journal of …, 2023 - ACS Publications
We apply the machine learning (ML) tool to calculate the Gibbs free energy (Δ G) of reaction
intermediates rapidly and accurately as a guide for designing porphyrin-and graphene …

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

W Xu, E Diesen, T He, K Reuter… - Journal of the American …, 2024 - ACS Publications
High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as
their unique active site distributions break the scaling relations that limit the activity of …

A computational study of electrochemical CO2 reduction to formic acid on metal-doped SnO2

Z Liu, X Zong, DG Vlachos, IAW Filot… - Chinese Journal of …, 2023 - Elsevier
Electrochemical reduction of CO 2 to formic acid (HCOOH) can contribute to the renewable
energy transition as a liquid carrier of renewably hydrogen. Here, we investigated the …

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

F Göltl, M Mavrikakis - ChemCatChem, 2022 - Wiley Online Library
Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, ie, a linear scaling between reaction
and activation energies, lie at the core of computational design of heterogeneous catalysts …

Machine learning assisted photothermal conversion efficiency prediction of anticancer photothermal agents

S Wu, Z Pan, X Li, Y Wang, J Tang, H Li, G Lu… - Chemical Engineering …, 2023 - Elsevier
Photothermal therapy (PTT) is a minimally invasive and promisingly effective strategy for
thermal ablation of tumors. There is an urgent need for the development of ideal organic …

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

A Rajan, AP Pushkar, BC Dharmalingam, JJ Varghese - Iscience, 2023 - cell.com
Modern heterogeneous catalysis has benefitted immensely from computational predictions
of catalyst structure and its evolution under reaction conditions, first-principles mechanistic …

Selective adsorption processes for fructooligosaccharides separation by activated carbon and zeolites through machine learning

ACFP Fuhr, Y Vieira, RC Kuhn, NPG Salau - … Engineering Research and …, 2023 - Elsevier
Fructooligosaccharides (FOS) separation and purification are crucial for industrial
applications where adsorption methods are widely used. However, some specific process …

Predicting hydrogenolysis reaction barriers of large hydrocarbons on metal surfaces using machine learning: Implications for polymer deconstruction

X Zong, T Xie, DG Vlachos - Applied Catalysis B: Environment and Energy, 2024 - Elsevier
Calculating activation energies of chemical reactions for large reaction networks is
computationally demanding. Traditional Brønsted-Evans-Polanyi (BEP) relationships are …