Machine Learning Interatomic Potentials for Catalysis DOI Creative Commons

Deqi Tang,

Rangsiman Ketkaew, Sandra Luber

et al.

Chemistry - A European Journal, Journal Year: 2024, Volume and Issue: 30(60)

Published: Aug. 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.

Language: Английский

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows DOI Creative Commons
Pavlo O. Dral, Fuchun Ge,

Yi-Fan Hou

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(3), P. 1193 - 1213

Published: Jan. 25, 2024

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, rapid development of ML methods requires flexible software framework for designing custom workflows. MLatom 3 program package designed to leverage power enhance typical chemistry simulations and create complex This open-source provides plenty choice users who can run with command-line options, input files, or scripts using as Python package, both on their computers online XACS cloud computing service at XACScloud.com. Computational chemists calculate energies thermochemical properties, optimize geometries, molecular quantum dynamics, simulate (ro)vibrational, one-photon UV/vis absorption, two-photon absorption spectra ML, mechanical, combined models. The choose from an extensive library containing pretrained models mechanical approximations such AIQM1 approaching coupled-cluster accuracy. developers build own various algorithms. great flexibility largely due use interfaces many state-of-the-art packages libraries.

Language: Английский

Citations

31
Dissecting the hydrogen bond network of water: Charge transfer and nuclear quantum effects DOI
Mischa Flór, David M. Wilkins, Miguel de la Puente

et al.

Science, Journal Year: 2024, Volume and Issue: 386(6726)

Published: Oct. 24, 2024

The molecular structure of water is dynamic, with intermolecular hydrogen (H) bond interactions being modified by both electronic charge transfer and nuclear quantum effects (NQEs). Electronic NQEs potentially change under acidic or basic conditions, but such details have not been measured. In this work, we developed correlated vibrational spectroscopy, a symmetry-based method that separates interacting from noninteracting molecules in self- cross-correlation spectra, giving access to previously inaccessible information. We found hydroxide (OH − ) donated ~8% more negative the H network water, hydronium (H 3 O + accepted ~4% less water. Deuterium oxide (D 2 O) had ~9% bonds compared O), solutions displayed dominant than ones.

Language: Английский

Citations

21
DPA-2: a large atomic model as a multi-task learner DOI Creative Commons
Duo Zhang, Xinzijian Liu, Xiangyu Zhang

et al.

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Dec. 19, 2024

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with accuracy of ab initio electronic structure methods. However, model generation process remains a bottleneck for large-scale applications. We propose shift towards model-centric ecosystem, wherein large (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned distilled various downstream tasks, thereby establishing new framework molecular modeling. In this study, we introduce DPA-2 architecture as prototype LAMs. Pre-trained on diverse array chemical materials systems using multi-task approach, demonstrates superior generalization capabilities tasks compared traditional single-task pre-training fine-tuning methodologies. Our approach sets stage development broad application LAMs simulation research.

Language: Английский

Citations

19
Constraints on the location of the liquid–liquid critical point in water DOI
Francesco Sciortino, Yaoguang Zhai, Sigbjørn Løland Bore

et al.

Nature Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

Citations

3
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023 DOI Creative Commons
Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

We present a comprehensive analysis of the capabilities modern machine learning force fields to simulate long-term molecular dynamics at near-ambient conditions for molecules, molecule-surface interfaces, and materials within TEA Challenge 2023.

Language: Английский

Citations

2
Nature-inspired hierarchical building materials with low CO2 emission and superior performance DOI Creative Commons
Jinyang Jiang, Han Wang, Junlin Lin

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 28, 2025

Abstract Conventional cement-based materials are faced with significant challenges, including large carbon emissions, high density, and quasi-brittleness. Here, inspired by hierarchical porous structures existing in nature, we develop a low carbon, lightweight, strong tough material (LLST), which is obtained rapid gelation of hydrogel as skeleton subsequent deposition cement hydrates skin. As result, the LLST exhibits structure consisting sponge-like micropores (1 ~ 50 μm) nanopores (5 100 nm), without detrimental macropores that compromise light weight, strength, toughness. Compared normal paste, displays 54% reduction 145% 1365% improvement specific compressive strength fracture energy, only 51% emission. These properties further investigated machine learning force field molecular dynamics along well-tempered metadynamics simulations, indicating chemical bonding generated at atomic level between functional groups Ca ion released from hydration. findings not demonstrate strategy for developing lightweight building low-carbon emission remarkable mechanical properties, but also provide valuable insights realizing coexistence toughness tailoring pore structure.

Language: Английский

Citations

2
Multiscale Modeling of Aqueous Electric Double Layers DOI Creative Commons
Maximilian Becker, Philip Loche, Majid Rezaei

et al.

Chemical Reviews, Journal Year: 2023, Volume and Issue: 124(1), P. 1 - 26

Published: Dec. 20, 2023

From the stability of colloidal suspensions to charging electrodes, electric double layers play a pivotal role in aqueous systems. The interactions between interfaces, water molecules, ions and other solutes making up electrical layer span length scales from Ångströms micrometers are notoriously complex. Therefore, explaining experimental observations terms layer's molecular structure has been long-standing challenge physical chemistry, yet recent advances simulations techniques computational power have led tremendous progress. In particular, past decades seen development multiscale theoretical framework based on combination quantum density functional theory, force-field continuum theory. this Review, we discuss these developments make quantitative comparisons results from, among techniques, sum-frequency generation, atomic-force microscopy, electrokinetics. Starting vapor/water interface, treat range qualitatively different types surfaces, varying soft solid, hydrophilic hydrophobic, charged uncharged.

Language: Английский

Citations

37
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations DOI
Raúl P. Peláez, Guillem Simeon, Raimondas Galvelis

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(10), P. 4076 - 4087

Published: May 14, 2024

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been persistent challenge. This paper presents substantial advancements TorchMD-Net software, pivotal step forward the shift from conventional force fields to neural network-based potentials. The evolution of into more comprehensive versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. transformation achieved through modular design approach, encouraging customized applications within scientific community. most notable enhancement significant improvement efficiency, achieving very remarkable acceleration computation energy forces for TensorNet models, with performance gains ranging 2× 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions smooth integration existing dynamics frameworks. Additionally, updated version introduces capability integrate physical priors, further enriching its application spectrum utility research. software available at https://github.com/torchmd/torchmd-net.

Language: Английский

Citations

16
Mechanism of Charge Transport in Lithium Thiophosphate DOI Creative Commons

Lorenzo Gigli,

Davide Tisi, Federico Grasselli

et al.

Chemistry of Materials, Journal Year: 2024, Volume and Issue: 36(3), P. 1482 - 1496

Published: Feb. 5, 2024

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li3PS4 are far from being fully understood, role PS4 dynamics charge still controversial. In this work, we build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r2SCAN, PBE0) tackle problem all known phases (α, β, γ), system sizes time scales. We discuss physical origin observed superionic behavior Li3PS4: activation flipping drives structural transition phase, characterized by an increase Li-site availability drastic reduction energy diffusion. also rule out any paddle-wheel effects tetrahedra phases─previously claimed enhance diffusion─due orders-of-magnitude difference between rate flips hops at temperatures below melting. finally elucidate interionic dynamical correlations transport, highlighting failure Nernst–Einstein approximation estimate electrical conductivity. Our results show strong dependence on target reference, with PBE0 yielding best quantitative agreement experimental measurements not only electronic band gap but conductivity β- α-Li3PS4.

Language: Английский

Citations

15
In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back DOI
Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García

et al.

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

Citations

15