Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

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Evolution and Reconstruction of Air‐Electrode Surface Composition in Reversible Protonic Ceramic Cells: Mechanisms, Impacts on Catalytic Performance, and Optimization Strategies – A Review

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Abstract Reversible protonic ceramic cells (R‐PCCs) are at the forefront of electrochemical conversion devices, capable reversibly and efficiently converting chemical energy into electricity intermediate temperatures (350–700 °C) with zero carbon emissions. However, slow surface catalytic reactions air‐electrode often hinder their performance durability. The electrode is not merely an extension bulk structure, equilibrium reconstruction can lead to significantly different crystal‐plane terminations morphologies, which influenced by material's intrinsic properties external reaction conditions. Understanding evolution elevated in water‐containing, oxidative atmospheres presents significant importance. In this review, a comprehensive summary recent processes applying advanced characterization techniques for high‐temperature surfaces provided, exploring correlations between fluctuations examining structural various associated degradation activation phenomena, offering insights impact on performance. Furthermore, reported strategies advances enhancing R‐PCCs through engineering discussed. This review offers valuable expected guide future developments catalysis, solid‐state ionics, materials.

Language: Английский

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Theoretical Design Strategies for Area-Selective Atomic Layer Deposition

Chemistry of Materials, Journal Year: 2024, Volume and Issue: 36(11), P. 5313 - 5324

Published: May 22, 2024

Area-selective atomic layer deposition (AS-ALD) is a bottom-up fabrication technique that may revolutionize the semiconductor manufacturing process. Because efficiency and applicability of AS-ALD strongly depend on properties molecular precursors for deposition, structural design optimization are needed. With aid various modern computational chemistry tools, tailor-made ALD high selectivity become possible. In this Perspective, requirements challenges precursors, as well theoretical strategies them, discussed. Current approaches analysis processes materials reviewed. A possible simulation strategy aspects suggested.

Language: Английский

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ViPErLEED package I: Calculation of I(V) curves and structural optimization

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 3, 2025

Low-energy electron diffraction (LEED) is a widely used technique in surface-science laboratories. Yet, it rarely to its full potential. The quantitative information about the surface structure, contained modulation of intensities diffracted beams as function incident energy, LEED I(V), underutilized. To acquire these data, only minor adjustments would be required most experimental setups, but existing analysis software cumbersome use and often computationally inefficient. ViPErLEED (Vienna package for Erlangen LEED) project lowers barriers, introducing combined solution user-friendly data acquisition, extraction, computational analysis. These parts are discussed three separate publications. Here, focus on part ViPErLEED, which performs highly automated LEED-I(V) calculations structural optimizations. Minimal user input required, functionality significantly enhanced compared solutions. Computation performed by embedding tensor-LEED (). manages additional parallelization, monitors convergence, processes all output. This makes I(V) more accessible new users while minimizing potential errors manual labor. Added functionalities include intelligent structure-dependent defaults calculation parameters, automatic detection bulk symmetries their relationship, search procedures that preserve symmetry speed up code handle larger systems than before, well parallelization optimization. Modern file formats output, there direct interface atomic simulation environment (ASE) package. implemented primarily (version ≥3.7) provided an open-source (GNU GPLv3 or any later version). A structure determination α-Fe2O3(11¯02)−(1×1) presented example application software. Published American Physical Society 2025

Language: Английский

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Adsorption of dimethylaluminum isopropoxide (DMAI) on the Al2O3 surface: A machine-learning potential study

Journal of Science Advanced Materials and Devices, Journal Year: 2024, Volume and Issue: 9(3), P. 100754 - 100754

Published: June 4, 2024

Dimethylaluminum isopropoxide (DMAI) is attracting attention as an alternative precursor for atomic layer deposition (ALD) of aluminum oxide (Al2O3). However, the surface chemical reaction mechanisms DMAI during ALD regarding its dimeric structure under vacuum process conditions has not been clear yet. In this work, adsorption mechanism and monomeric on a fully hydroxylated Al2O3 studied using machine-learning potential (MLP) calculations. The initial appears facile, would result in coexistence both methyl isopropoxy ligands surface. reactivity smaller than TMA, owing to propensity adopt form. Especially, when substrate partially covered by other adsorbate species, large molecular size low considerably hinder toward adsorption. Current results are good correspondence with previous experiemental results, where lower growth per cycle (GPC) higher selectivity area-selective (AS-ALD) could be observed compared those TMA processes.

Language: Английский

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Density Functional Theory Study of Surface Stability and Phase Diagram of Orthorhombic CsPbI₃

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 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

Language: Английский

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Understanding Surface/Interface‐Induced Chemical and Physical Properties at Atomic Level by First Principles Investigations

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2025, Volume and Issue: 15(3)

Published: May 1, 2025

ABSTRACT The scientific trajectory in contemporary materials research has transitioned toward surface and interface engineering as critical determinants of functional performance, facilitating atomic‐level precision modulating physical chemical properties for advanced applications spanning device architectures, catalytic systems, electrochemical technologies. However, persistent challenges atomic‐scale characterization the resource‐intensive nature empirical optimization necessitate systematic implementation first‐principles calculations to elucidate fundamental mechanisms underlying experimental observations enable rational design surface/interface modifications. This review examines three advancements ab initio interfacial engineering: (1) revealing mechanism selective assembly activation phenomena on surfaces, (2) theoretical predictions strategies, (3) developing material databases with ionic/van der Waals components. We further address computational while proposing quantum‐mechanical methods next‐gen customized properties.

Language: Английский

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Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(15), P. 6813 - 6825

Published: July 29, 2024

The machine learning potential has emerged as a promising approach for addressing the accuracy-versus-efficiency dilemma in molecular modeling. Efficiently exploring chemical spaces with high accuracy presents significant challenge, particularly interface reaction system. This study introduces workflow aimed at achieving this goal by incorporating classical SOAP descriptor and practical PCA strategy to minimize redundancy data requirements, while successfully capturing features of complex energy surfaces. Specifically, focuses on combustion behaviors within alloy-based solid propellants. A neural network model tailored modeling AlLi–AP reactions under varying conditions is constructed, showcasing excellent predictive capabilities prediction, force estimation, bond energies. series large-scale MD simulations reveal that Li doping significantly influences initial stage, enhancing reactivity reducing thermal conductivity. Mass transfer analysis also highlights considerably higher diffusion coefficient compared Al, former being three times greater. Consequently, overall process accelerated approximately 10%. These breakthroughs pave way virtual screening rational design advanced propellant formulations microstructures alloy-formula

Language: Английский

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Computationally Guided Synthesis of Battery Materials

ACS Energy Letters, Journal Year: 2024, Volume and Issue: 9(6), P. 2902 - 2911

Published: May 22, 2024

Materials synthesis is a critical step in the development of energy storage technologies, from first newly predicted materials to optimization key properties for established materials. While solid-state has traditionally relied on intuition-driven trial-and-error, computational approaches are now emerging accelerate identification improved recipes. In this Perspective, we explore these techniques and focus their ability guide precursor selection synthesis. The applicability each method discussed context batteries, including Li-ion cathodes solid electrolytes all-solid-state batteries. Our analysis showcases effectiveness methods while also highlighting limitations. Based findings, provide an outlook future developments that can address existing limitations make progress toward synthesis-by-design battery

Language: Английский

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