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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Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 135(16)

Published: April 24, 2024

Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting is the use accurate efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise providing required accuracy a broad range In this mini-review tutorial, we delve into fundamentals transport, explore pertinent MD simulation methods, survey applications MLPs transport. Furthermore, provide step-by-step tutorial on developing highly predictive simulations, utilizing neuroevolution as implemented GPUMD package. Our aim with to empower researchers valuable insights cutting-edge methodologies that can significantly enhance efficiency studies.

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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Machine Learning Committee Neural Network Potential Energy Surfaces for Two-Dimensional Metal–Organic Frameworks

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

Published: Jan. 15, 2025

Two-dimensional (2D) layered metal–organic frameworks (MOFs) are gaining attention due to their unique structural and electronic properties with promising applications in compact device fabrication. Long-time large-scale molecular dynamics simulations of these materials can enhance expedite the mapping out structure–property–function relationships for applications. To make such more feasible, herein, we construct a high-dimensional committee neural network potential (CNNP) archetypal 2D MOFs Ni3(HIB)2 Ni3(HITP)2 where HIB = hexaiminobenzene HITP hexaiminotriphenylene. We harness power active learning networks obtain CNNP model by using only hundreds snapshots from ab initio (AIMD) trajectories. The developed allows thousands atoms over extended time scales, which is typically unfeasible AIMD while maintaining accuracy reference data. Our stable MD based on reveal flexible nature studied at room temperature, including puckered layers, as opposed planar ones 0 K structure calculations. Furthermore, our demonstrates transferability between bulk monolayers, well different organic linkers. As first its kind, show that models could be reliable effective approach future studies MOFs.

Language: Английский

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Combining Brillouin Light Scattering Spectroscopy and Machine-Learned Interatomic Potentials to Probe Mechanical Properties of Metal-Organic Frameworks

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 1213 - 1220

Published: Jan. 25, 2025

The mechanical properties of metal-organic frameworks (MOFs) are high fundamental and practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has rarely been used highly complex materials like MOFs. In the present contribution, we apply this to study a newly synthesized MOF-type material, referred as GUT2. experiments combined with state-of-the-art simulations phonon bands, based on machine-learning force fields dispersion-corrected density functional theory. This provides comprehensive understanding experimental signals, can be correlated longitudinal transverse sound velocities material. Notably, combination insights from allows determination approximate values components tensor studied material even when dealing comparably small single crystals, limit range accessible data.

Language: Английский

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Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials

Communications Materials, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 2, 2025

Language: Английский

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Insights into defect cluster formation in non-stoichiometric wustite (Fe1−xO) at elevated temperatures: accurate force field from deep learning

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 14, 2025

Language: Английский

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Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 14, 2025

Language: Английский

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Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis

Published: Jan. 1, 2025

Language: Английский

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On simulating thin-film processes at the atomic scale using machine-learned force fields

Journal of Vacuum Science & Technology A Vacuum Surfaces and Films, Journal Year: 2025, Volume and Issue: 43(3)

Published: March 24, 2025

Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms the but also to extract quantitative metrics on events and reactions taking place at gas-surface interface. Molecular dynamics is a powerful computational method study evolution process atomic scale, studies industrially relevant usually require suitable force fields, which are, in general, available all interest. However, machine-learned fields (MLFFs) are conquering field materials surface science. In this paper, we demonstrate how efficiently build MLFFs simulations provide two examples technologically processes: precursor pulse layer deposition HfO2 etching MoS2.

Language: Английский

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