Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(14), P. 8620 - 8656

Published: July 11, 2024

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model an atomic level. Here, we review emerging atomistic methods simulate electrocatalytic interface with special attention devoted components/effects have been model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, pH. Additionally, relevant computational spectroscopy methods. Then, showcase several examples applying these understand design catalysts green hydrogen. We also offer experimental views on how bridge gap between theory experiments. Finally, provide some perspectives opportunities advance field.

Language: Английский

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Ab initio characterization of protein molecular dynamics with AI2BMD

Nature, Journal Year: 2024, Volume and Issue: 635(8040), P. 1019 - 1027

Published: Nov. 6, 2024

Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on accuracy efficiency

Language: Английский

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Machine Learning of Reactive Potentials

Annual Review of Physical Chemistry, Journal Year: 2024, Volume and Issue: 75(1), P. 371 - 395

Published: June 28, 2024

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction training of MLPs enable fast accurate simulations analysis thermodynamic kinetic properties. This review focuses on application to reaction systems with consideration bond breaking formation. We development MLP models, primarily neural network kernel-based algorithms, recent applications reactive (RMLPs) at different scales. show how RMLPs are constructed, they speed up calculation dynamics, facilitate study trajectories, rates, free energy calculations, many other calculations. Different data sampling strategies applied building also discussed a focus collect structures for rare events further improve their performance active learning.

Language: Английский

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Nonlocal Models in Biology and Life Sciences: Sources, Developments, and Applications

Physics of Life Reviews, Journal Year: 2025, Volume and Issue: 53, P. 24 - 75

Published: Feb. 27, 2025

Language: Английский

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Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(43)

Published: Oct. 17, 2023

Abstract This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially key methodologies density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses how periodic cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus predicting structural transformations, understanding thermodynamic properties catalysis, providing information or that are fed classical simulations such as force field parameters partial charges. Classical simulation methods, highlighting selection, databases MOFs for high‐throughput screening, synergistic nature MC MD described. By equilibrium dynamic these methods offer wide perspective behavior mechanisms. Additionally, incorporation machine learning (ML) techniques quantum is discussed. These enhance accuracy, expedite setup, reduce computational costs, well predict parameters, optimize geometries, estimate stability. charting growth promise field, aim to recommendations facilitate more broadly research.

Language: Английский

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Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: 128(5), P. 945 - 957

Published: Jan. 26, 2024

A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination low-energy structures, their high computational cost problematic because need to evaluate tens hundreds thousands trial structures fully explore typical energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as first stage a hierarchical scheme involving multiple stages increasingly calculations. Machine-learned interatomic potentials (MLIPs), trained reproduce results ab initio with costs close those fields, can improve efficiency CSP by reducing or eliminating costly DFT Here, we investigate active learning training MLIPs datasets. The combination well-developed sampling from yields highly automated workflow that relevant over wide range packing space. To demonstrate these potentials, illustrate efficiently reranking large, diverse landscapes near-DFT accuracy field-based CSP, improving reliability final ranking. Furthermore, how be extended more model far lattice minima through additional on-the-fly within Monte Carlo simulations.

Language: Английский

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Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(24), P. 13681 - 13714

Published: Nov. 21, 2024

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides ML-based interatomic potentials have paved the way accurate modeling diverse chemical structural at atomic level. key determinant defining MLIP reliability remains quality training data. A paramount challenge lies constructing sets that capture specific domains vast space. This Review navigates intricate landscape essential components integrity data ensure extensibility transferability resulting models. We delve into details active learning, discussing its various facets implementations. outline different types uncertainty quantification applied to atomistic acquisition correlations between estimated true error. role samplers generating informative structures highlighted. Furthermore, we discuss via modified surrogate potential energy surfaces as innovative approach diversify also provides a list publicly available cover

Language: Английский

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The Potential of Neural Network Potentials

ACS Physical Chemistry Au, Journal Year: 2024, Volume and Issue: 4(3), P. 232 - 241

Published: March 21, 2024

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by combination of recent advances in quantum and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are breakthrough new tool that already enabling us to simulate systems at molecular scale with unprecedented accuracy speed, relying on nothing but fundamental laws. The continued development this approach realize Paul Dirac's 80-year-old vision using mechanics unify physics providing invaluable tools for understanding materials science, biology, earth sciences, beyond. era highly accurate efficient first-principles simulations provide wealth training data can be used build automated computational methodologies, such as diffusion models, design optimization scale. Large language models (LLMs) also evolve into increasingly indispensable literature review, coding, idea generation, scientific writing.

Language: Английский

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In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(30)

Published: May 25, 2024

Abstract Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving Schrödinger equations increasing cost with size molecular system. In response, there has been a surge interest in leveraging artificial intelligence (AI) machine learning (ML) techniques silico experiments. Integrating AI ML into increases scalability speed exploration space. remain, particularly regarding reproducibility transferability models. This review highlights evolution from, complementing, or replacing energy property predictions. Starting from models trained entirely on numerical data, journey set forth toward ideal model incorporating physical laws quantum mechanics. paper also reviews existing their intertwining, outlines roadmap future research, identifies areas improvement innovation. Ultimately, goal develop architectures capable accurate transferable solutions equation, thereby revolutionizing experiments within materials science.

Language: 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, Journal Year: 2024, Volume and Issue: 128(16), P. 6524 - 6537

Published: March 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.

Language: Английский

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