High-throughput and machine learning approaches for the discovery of metal organic frameworks DOI Creative Commons
Xiangyu Zhang, Zezhao Xu,

Zidi Wang

et al.

APL Materials, Journal Year: 2023, Volume and Issue: 11(6)

Published: June 1, 2023

Metal-organic frameworks (MOFs) are promising nanoporous materials with diverse applications. Traditional material discovery based on intensive manual experiments has certain limitations efficiency and effectiveness when faced nearly infinite space. The current situation offers an opportunity for high-throughput (HT) machine learning (ML) approaches, including computational experimental methods, as they have greatly improved the of MOF screening capacity to deal enormous growth data. In this review, we discuss research progress in HT computation their effect discovery. We also highlight how ML-based approaches integration methods ML algorithms accelerate design. addition, provide our insights future capability data-driven techniques discovery, despite facing some knowledge gaps obstacle.

Language: Английский

Activity and Selectivity Roadmap for C–N Electro-Coupling on MXenes DOI
Yiran Jiao, Haobo Li, Yan Jiao

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(28), P. 15572 - 15580

Published: July 6, 2023

Electrochemical coupling between carbon and nitrogen species to generate high-value C-N products, including urea, presents significant economic environmental potentials for addressing the energy crisis. However, this electrocatalysis process still suffers from limited mechanism understanding due complex reaction networks, which restricts development of electrocatalysts beyond trial-and-error practices. In work, we aim improve mechanism. This goal was achieved by constructing activity selectivity landscape on 54 MXene surfaces density functional theory (DFT) calculations. Our results show that step is largely determined *CO adsorption strength (Ead-CO), while relies more co-adsorption *N (Ead-CO Ead-N). Based these findings, propose an ideal catalyst should satisfy moderate stable adsorption. Through machine learning-based approach, data-driven formulas describing relationship Ead-CO Ead-N with atomic physical chemistry features were further identified. identified formula, 162 materials screened without time-consuming DFT Several potential catalysts predicted good performance, such as Ta2W2C3. The candidate then verified study has incorporated learning methods first time provide efficient high-throughput screening method selective electrocatalysts, could be extended a wider range electrocatalytic reactions facilitate green chemical production.

Language: Английский

Citations

57
Machine Learning Descriptors for Data‐Driven Catalysis Study DOI Creative Commons

Li‐Hui Mou,

TianTian Han,

Pieter E. S. Smith

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(22)

Published: May 16, 2023

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful predictive abilities. The selection of appropriate input features (descriptors) plays decisive role in improving the accuracy ML models uncovering key factors that influence activity selectivity. This review introduces tactics utilization extraction descriptors ML-assisted experimental research. In addition effectiveness advantages various descriptors, their limitations are also discussed. Highlighted both 1) newly developed spectral performance prediction 2) novel paradigm combining computational through suitable intermediate descriptors. Current challenges future perspectives on application techniques presented.

Language: Английский

Citations

51
Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy DOI

Xinyuan Bi,

Li Lin, Zhou Chen

et al.

Small Methods, Journal Year: 2023, Volume and Issue: 8(1)

Published: Oct. 27, 2023

Abstract Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in broad range of fields including biomedicine, environmental protection, food safety among the others. In endless pursuit ever‐sensitive, robust, comprehensive sensing imaging, advancements keep emerging whole pipeline SERS, from design SERS substrates reporter molecules, synthetic route planning, instrument refinement, to data preprocessing analysis methods. Artificial intelligence (AI), which is created imitate eventually exceed human behaviors, exhibited its power learning high‐level representations recognizing complicated patterns with exceptional automaticity. Therefore, facing up intertwining influential factors explosive size, AI been increasingly leveraged all above‐mentioned aspects presenting elite efficiency accelerating systematic optimization deepening understanding about fundamental physics spectral data, far transcends labors conventional computations. this review, recent progresses are summarized through integration AI, new insights challenges perspectives provided aim better gear toward fast track.

Language: Английский

Citations

46
Nature of metal-support interaction for metal catalysts on oxide supports DOI
Tairan Wang,

Jianyu Hu,

Runhai Ouyang

et al.

Science, Journal Year: 2024, Volume and Issue: 386(6724), P. 915 - 920

Published: Nov. 21, 2024

The metal-support interaction is one of the most important pillars in heterogeneous catalysis, but developing a fundamental theory has been challenging because intricate interfaces. Based on experimental ‎data, interpretable machine learning, theoretical derivation, and first-principles simulations, we established ‎general metal-oxide interactions grounded ‎metal-metal metal-oxygen interactions. applies to metal nanoparticles atoms oxide supports films supports. We found that for late-transition catalysts, metal-metal dominated support effects suboxide encapsulation over nanoparticles. A principle strong occurrence formulated substantiated by extensive ‎experiments including 10 metals 16 ‎oxides. valuable insights revealed (strong) advance interfacial design supported catalysts.

Language: Английский

Citations

32
Recent developments and current trends on catalytic dry reforming of Methane: Hydrogen Production, thermodynamics analysis, techno feasibility, and machine learning DOI

Mohammed Mosaad Awad,

Esraa Kotob,

Omer Ahmed Taialla

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 304, P. 118252 - 118252

Published: Feb. 29, 2024

Language: Английский

Citations

27
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design DOI
Jorge Benavides-Hernández, Franck Dumeignil

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(15), P. 11749 - 11779

Published: July 24, 2024

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

Language: Английский

Citations

24
Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts DOI Creative Commons
Haobo Li, Yan Jiao, Kenneth Davey

et al.

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 62(9)

Published: Dec. 13, 2022

Abstract The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved via optimization adsorption energy and microkinetic modelling. A prerequisite to ensure the physically meaningful stable existence conceived active‐site structure on surface. development improved understanding catalyst surface, however, challenging practically because complex nature dynamic surface formation evolution under in‐situ reactions. We propose therefore data‐driven machine‐learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using search predict (meta)stable structures, assist operando simulation reaction conditions micro‐environments, critically analyze experimental characterization data. conclude that ML will become new norm lower costs associated with discovery optimal catalysts.

Language: Английский

Citations

69
Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors DOI Open Access
Song Wang, Jun Jiang

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(11), P. 7428 - 7436

Published: May 18, 2023

The complexity and dynamics of catalytic systems make it challenging to study the catalysts reactions. Fortunately, advance machine learning (ML) has made descriptor-based catalyst screening rational design a mainstream research approach. Herein, spectroscopic descriptors reported in recent years are highlighted field catalysis. Both vibrational spectra X-ray absorption have demonstrated strong ability predict structures properties. Through several cases, interpretable ML models based on discussed reveal physical knowledge mechanism exhibit superiority transfer tasks imperfect data scenarios. Finally, this Viewpoint, we illustrate challenges with provide perspectives.

Language: Английский

Citations

28
Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning DOI
Keerthana Vellayappan, Yifei Yue, Kang Hui Lim

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 330, P. 122593 - 122593

Published: March 7, 2023

Language: Английский

Citations

25
Automation and machine learning augmented by large language models in a catalysis study DOI Creative Commons
Yuming Su, Xue Wang,

Yuanxiang Ye

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(31), P. 12200 - 12233

Published: Jan. 1, 2024

AI and automation are revolutionizing catalyst discovery, shifting from manual methods to high-throughput digital approaches, enhanced by large language models.

Language: Английский

Citations

13