Title :

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

Author(s) :

Benavides-Hernández, J. [Auteur]
Unité de Catalyse et Chimie du Solide - UMR 8181 [UCCS]
Dumeignil, Franck [Auteur]
Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Benavides-Hernández, J. [Auteur]

Journal title :

ACS Catalysis

Abbreviated title :

ACS Catal.

Volume number :

14

Pages :

11749–11779

Publication date :

2024-11-18

ISSN :

2155-5435

English keyword(s) :

artificial intelligence
machine learning
high-throughput experimentation
heterogeneous catalysts
catalyst design
deep learning
optimization
high-throughput screening

HAL domain(s) :

Chimie/Catalyse

English abstract : [en]

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field of heterogeneous catalysis, presenting a broad spectrum ...
Show more >This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field of heterogeneous catalysis, presenting a broad spectrum of contemporary methodologies and innovations. We methodically segmented the text into three core areas: catalyst characterization, data-driven exploitation, and data-driven discovery. In the catalyst characterization part, we outline current and prospective techniques used for HTE and how AI-driven strategies can streamline or automate their analysis. The data-driven exploitation part is divided into themes, strategies, and techniques that offer flexibility for either modular application or creation of customized solutions. In the data-driven exploration part we present applications that enable exploration of areas outside the experimentally tested chemical space, incorporating a section on computational methods for identifying new prospects. The review concludes by addressing the current limitations within the field and suggesting possible avenues for future research.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
Centrale Lille
ENSCL
Univ. Artois

Collections :

• Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181

Submission date :

2024-11-20T22:03:55Z
2024-11-29T09:30:07Z