Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects

Chemical Engineering Journal, Год журнала: 2024, Номер 494, С. 152757 - 152757

Опубликована: Июнь 2, 2024

Язык: Английский

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Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

The Journal of Physical Chemistry C, Год журнала: 2024, Номер 128(16), С. 6524 - 6537

Опубликована: Март 20, 2024

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack clear standards current literature. In this Perspective, we aim provide guidance on best practices documenting use while walking the reader through development deployment including hardware software requirements, generating training data, models, validating predictions, inference. We also suggest useful plotting analyses validate boost confidence deployed models. Finally, step-by-step checklist practitioners directly before publication standardize information be reported. Overall, hope that our work will encourage reliable reproducible these MLIPs, which accelerate their ability make positive impact various disciplines science, chemistry, biology, among others.

Язык: Английский

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Machine Learning Interatomic Potentials for Catalysis

Chemistry - A European Journal, Год журнала: 2024, Номер 30(60)

Опубликована: Авг. 7, 2024

Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in areas of, e. g., chemistry, materials science, and biology. Classical force fields ab initio calculations have been widely adopted molecular simulations. However, these methods usually suffer from drawbacks either low accuracy or high cost. Recently, development machine learning interatomic potentials (MLIPs) has become more popular they tackle problems question deliver rather accurate results at significantly lower computational In this review, atomistic catalytic systems with aid MLIPs is discussed, showcasing recently developed MLIP models selected applications for systems. We also highlight best practices challenges give an outlook future works on field catalysis.

Язык: Английский

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Probing intermediate configurations of oxygen evolution catalysis across the light spectrum

Nature Energy, Год журнала: 2024, Номер 9(10), С. 1191 - 1198

Опубликована: Авг. 26, 2024

Язык: Английский

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Unveiling Competitive Adsorption in TiO₂ Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods

ACS Applied Materials & Interfaces, Год журнала: 2024, Номер 16(28), С. 36215 - 36223

Опубликована: Июль 2, 2024

The efficient harnessing of solar power for water treatment via photocatalytic processes has long been constrained by the challenge understanding and optimizing interactions at photocatalyst surface, particularly in presence nontarget cosolutes. adsorption these cosolutes, such as natural organic matter, onto photocatalysts can inhibit degradation pollutants, drastically decreasing efficiency. In present work, computational methods are employed to predict inhibitory action a suite small molecules during TiO2 para-chlorobenzoic acid (pCBA). Specifically, tryptophan, coniferyl alcohol, succinic acid, gallic trimesic were selected interfering agents against pCBA observe resulting competitive reaction kinetics bulk surface phase reactions according Langmuir–Hinshelwood dynamics. Experiments revealed that acids most with pCBA, followed acid. Density functional theory (DFT) machine learning interatomic potentials (MLIPs) used investigate molecular basis interactions. findings showed while type group did not directly affinity, spatial arrangement electronic groups significantly influenced dynamics corresponding behavior. Notably, MLIPs, derived fine-tuning models pretrained on vastly larger dataset, enabled exploration behaviors over substantially longer periods than typically possible conventional ab initio dynamics, enhancing depth dynamic interaction processes. Our study thus provides pivotal foundation advancing technology environmental applications demonstrating critical role molecular-level shaping outcomes.

Язык: Английский

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Machine Learning Potentials for Heterogeneous Catalysis

ACS Catalysis, Год журнала: 2025, Номер 15(3), С. 1616 - 1634

Опубликована: Янв. 15, 2025

The production of many bulk chemicals relies on heterogeneous catalysis. rational design or improvement the required catalysts critically depends insights into underlying mechanisms atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions operando, but order achieve a comprehensive understanding, additional information from computer simulations is indispensable cases. particular, ab initio molecular dynamics (AIMD) become an important tool explicitly address atomistic level structure, dynamics, and reactivity interfacial systems, high computational costs limit applications systems consisting at most few hundred atoms for simulation times up tens picoseconds. Rapid advances development modern machine learning potentials (MLP) now offer promising approach bridge this gap, enabling with accuracy small fraction costs. Perspective, we provide overview current state art MLPs relevant catalysis along discussion prospects use science years come.

Язык: Английский

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Mechanistic Insights and Advances in Electrode/Electrolyte Interfaces for Efficient Electrocatalytic CO₂ Reduction to C₂ Products

SmartMat, Год журнала: 2025, Номер 6(1)

Опубликована: Фев. 1, 2025

ABSTRACT Electrocatalytic CO 2 reduction (ECR) is a promising approach to converting into chemicals and fuels. Among the ECR products, C products such as ethylene, ethanol, acetate have been extensively studied due their high industrial demands. However, mechanistic understanding of product formation remains unclear lack in situ or operando measurements that can observe complex instantaneous atomic evolutions adsorbates at electrode/electrolyte interface. Moreover, sensitivity reactions variations interface further widens gap between performance enhancement. To bridge this gap, first‐principle studies provide insights how influences ECR. In study, we present review investigating effects various factors interface, with an emphasis on formation. We begin by introducing essential metrics. Next, discuss classified components namely, electrocatalyst, electrolyte, adsorbates, respectively, Due interplay among these factors, aim deconvolute influence each factor clearly demonstrate impacts. Finally, outline directions for products.

Язык: Английский

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Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts

Nature Chemical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Язык: Английский

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Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis

Опубликована: Янв. 1, 2025

Язык: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Март 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Язык: Английский

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