Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Advanced Functional Materials, Journal Year: 2022, Volume and Issue: 32(17)

Published: Jan. 15, 2022

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative structure–activity relationships to accelerate electrocatalyst design by from historic data without explicit programming. The algorithms, data/database, and descriptors are usually the decisive factors ML play pivotal role electrocatalysis they contain essence of catalysis physicochemical nature. Despite considerable research efforts regarding with ML, lack universal selection tactics bridging gap between structures activity impedes its wider application. A timely summary application in helps deepen understanding nature improve scope efficiency. This review summarizes geometrical, electronic, used input training predicting reveal general rules their electrocatalysts. In response challenges hydrogen evolution reaction, oxygen reduction CO 2 nitrogen these areas tracked progress prospective changes. Additionally, potential automated discovery discussed other well‐known electrocatalytic processes.

Language: Английский

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Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Advanced Functional Materials, Journal Year: 2022, Volume and Issue: 32(31)

Published: May 18, 2022

Abstract Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome drawbacks SACs low loading isolated active site). The design DACs, however, remains cost‐prohibitive for both experimental computational studies, due to their huge space. Herein, by means density functional theory (DFT) topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput principle evaluate stability activity 16 767 DACs oxygen evolution (OER) reduction (ORR) reactions. rational reveals 511 types with OER superior IrO 2 (110), 855 ORR Pt (111), 248 bifunctional catalytic performance ORR. An intrinsic descriptor is revealed correlate DAC electronic structures its bonding carbon surface structure. This approach yields remarkable prediction precision (>0.926 R‐squared) enables notable 144 000‐fold screening time compared pure DFT calculations, holding promise drastically accelerate high‐performance DACs.

Language: Английский

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Machine Learning: A New Paradigm in Computational Electrocatalysis

The Journal of Physical Chemistry Letters, Journal Year: 2022, Volume and Issue: 13(34), P. 7920 - 7930

Published: Aug. 18, 2022

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, uncovering scientific insights lie the center of development electrocatalysis. Despite certain success in experiments computations, it is still difficult to achieve above objectives due complexity systems vastness chemical space for candidate electrocatalysts. With advantage machine learning (ML) increasing interest electrocatalysis energy conversion storage, data-driven research motivated by artificial intelligence (AI) has provided new opportunities discover promising investigate dynamic reaction processes, extract knowledge from huge data. In this Perspective, we summarize recent applications ML electrocatalysis, including electrocatalysts simulation processes. Furthermore, interpretable methods are discussed accelerate generation. Finally, blueprint envisaged future

Language: Английский

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Machine learning for design principles for single atom catalysts towards electrochemical reactions

Journal of Materials Chemistry A, Journal Year: 2022, Volume and Issue: 10(29), P. 15309 - 15331

Published: Jan. 1, 2022

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom (SACs) through establishment deep structure–activity relationships.

Language: Английский

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Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO₂ Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(14), P. 9616 - 9628

Published: July 7, 2023

Electrocatalytic CO2 reduction reactions (CO2RR) based on scalable and highly efficient catalysis provide an attractive strategy for reducing emissions. In this work, we combined first-principles density functional theory (DFT) machine learning (ML) to comprehensively explore the potential of double-atom catalysts (DACs) featuring inverse sandwich structure anchored defective graphene (gra) catalyze CO2RR generate C1 products. We started with five homonuclear M2⊥gra (M = Co, Ni, Rh, Ir, Pt), followed by 127 heteronuclear MM′⊥gra Pt, M′ Sc–Au). Stable DACs were screened evaluating their binding energy, formation dissolution metal atoms, as well conducting molecular dynamics simulations without solvent water molecules. Based DFT calculations, Rh2⊥gra DAC was found outperform other four Rh-based single- noninverse structures. Out DACs, 14 be stable have good catalytic performance. An ML approach adopted correlate key factors activity stability including sum radii ligand atoms (dM–M′, dM–C, dM′–C), difference electronegativity two (PM + PM′, PM – PM′), first ionization energy (IM IM′, IM IM′), electron affinity (AM AM′, AM AM′), number d-electrons (Nd). The obtained models further used predict 154 electrocatalysts out 784 possible same configuration. Overall, work not only identified promising reported but also provided insights into atomic characteristics associated high activity.

Language: Английский

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Machine Learning Descriptors for Data‐Driven Catalysis Study

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

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AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

ACS Nano, Journal Year: 2023, Volume and Issue: 17(11), P. 9763 - 9792

Published: June 2, 2023

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth data, adoption exploitation of artificial intelligence (AI) as part materials research framework had tremendous impact on development nanomaterials. AI has enabled revolutionary next-generation paradigms significantly accelerate all stages material discovery facilitate exploration enormous design space. In this review, we summarize recent advancements applications discovery, with special emphasis selected nanotechnology net-zero future including solar cells, hydrogen energy, battery renewable CO2 capture conversion capture, utilization storage (CCUS) addition, discuss limitations challenges current area by identifying gaps that exist development. Finally, present prospect directions order large-scale

Language: Английский

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Understanding the hydrogen evolution reaction activity of doped single-atom catalysts on two-dimensional GaPS4 by DFT and machine learning

Journal of Energy Chemistry, Journal Year: 2023, Volume and Issue: 81, P. 93 - 100

Published: Feb. 16, 2023

Language: Английский

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Advanced electrocatalytic technologies for conversion of carbon dioxide into methanol by electrochemical reduction: Recent progress and future perspectives

Coordination Chemistry Reviews, Journal Year: 2023, Volume and Issue: 482, P. 215081 - 215081

Published: Feb. 28, 2023

Language: Английский

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Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 917, P. 170085 - 170085

Published: Jan. 15, 2024

Language: Английский

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