Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

The Journal of Chemical Physics, Год журнала: 2025, Номер 162(6)

Опубликована: Фев. 12, 2025

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity often makes it challenging to address potential finite-size effects. Here, we present a specialized graphics processing units (GPUs) implementation of PIMD methods, including ring-polymer (RPMD) and thermostatted (TRPMD), into the open-source Graphics Processing Units Molecular Dynamics (GPUMD) package, combined with highly accurate efficient machine-learned neuroevolution (NEP) models. This approach achieves almost accuracy first-principles calculations efficiency empirical potentials, enabling large-scale atomistic that incorporate effects, effectively overcoming limitations at relatively affordable cost. We validate demonstrate efficacy NEP-PIMD by examining various thermal properties diverse materials, lithium hydride (LiH), three porous metal–organic frameworks (MOFs), liquid water, elemental aluminum. For LiH, our successfully capture isotope effect, reproducing experimentally observed dependence lattice parameter on reduced mass. MOFs, results reveal achieving good agreement experimental data requires consideration both dispersive interactions. significant impact its microscopic structure. aluminum, TRPMD method captures expansion phonon properties, aligning well mechanical predictions. GPU-accelerated GPUMD package provides an alternative, accessible, accurate, scalable tool exploring complex material influenced applications across broad range

Язык: Английский

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PLUMED Tutorials: A collaborative, community-driven learning ecosystem

The Journal of Chemical Physics, Год журнала: 2025, Номер 162(9)

Опубликована: Март 4, 2025

In computational physics, chemistry, and biology, the implementation of new techniques in shared open-source software lowers barriers to entry promotes rapid scientific progress. However, effectively training users presents several challenges. Common methods like direct knowledge transfer in-person workshops are limited reach comprehensiveness. Furthermore, while COVID-19 pandemic highlighted benefits online training, traditional tutorials can quickly become outdated may not cover all software’s functionalities. To address these issues, here we introduce “PLUMED Tutorials,” a collaborative model for developing, sharing, updating tutorials. This initiative utilizes repository management continuous integration ensure compatibility with updates. Moreover, interconnected form structured learning path enriched automatic annotations provide broader context. paper illustrates development, features, advantages PLUMED Tutorials, aiming foster an open community creating sharing educational resources.

Язык: Английский

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DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance had profound impact in applications that include drug discovery, enzyme catalysis, materials design. The current landscape of MLP software presents challenges due the limited interoperability between packages, which can lead inconsistent benchmarking practices necessitates separate interfaces with dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin DeePMD-kit framework extends its capabilities support external graph neural network (GNN) potentials.DeePMD-GNN enables seamless integration popular GNN-based models, such as NequIP MACE, within ecosystem. Furthermore, new infrastructure allows GNN be used combined quantum mechanical/molecular mechanical (QM/MM) using range corrected ΔMLP formalism.We demonstrate application DeePMD-GNN performing benchmark calculations NequIP, DPA-2 developed under consistent training conditions ensure fair comparison.

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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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 2, 2025

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations related applications. These packages, typically built on specific frameworks, such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation DeePMD-kit exemplified these limitations. this work, we introduce version 3, a significant update featuring multibackend framework that supports PaddlePaddle backends, demonstrate versatility architecture through other MLP differentiable force fields. This allows seamless back-end switching with minimal modifications, enabling users developers to integrate using innovation facilitates more complex interoperable workflows, paving way broader MLPs scientific research.

Язык: Английский

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Nuclear Quantum Effects in Neutral Water Clusters at Finite Temperature: Structural Evolution from Two to Three Dimensions

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 3004 - 3011

Опубликована: Март 17, 2025

Understanding the structure of bulk water presents a significant challenge due to its intricate hydrogen bond network and dynamic properties. Neutral clusters, serving as fundamental building blocks, provide key insights into configurations intermolecular interactions, thereby establishing critical foundation for elucidating behavior liquid water. In this study, state-of-the-art quantum simulations utilizing many-body potential are employed investigate influence nuclear effects (NQEs) on structural evolution neutral clusters (H2O)n (n = 2–10). For pentamer at finite temperature, demonstrate that NQEs substantially facilitate transition from two-dimensional (2D) three-dimensional (3D) configurations. The population 3D isomers is governed by synergistic interplay among thermal fluctuates NQEs. hexamers with fully structures, uncover lower-energy pathway prism book via cage-like intermediate―a not observed in classical simulations. These findings highlight crucial role theoretical framework explore properties condensed-phase

Язык: Английский

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Advances in theory and computational methods for next-generation thermoelectric materials

Applied Physics Reviews, Год журнала: 2025, Номер 12(1)

Опубликована: Март 1, 2025

This is a review of theoretical and methodological development over the past decade pertaining to computational characterization thermoelectric materials from first principles. Primary focus on electronic thermal transport in solids. Particular attention given relationships between various methods terms hierarchy as well tradeoff physical accuracy efficiency each. Further covered are up-and-coming for modeling defect formation dopability, keys realizing material's potential. We present discuss all these close connection with parallel developments high-throughput infrastructure code implementation that enable large-scale computing screening. In all, it demonstrated advances tools now ripe efficient accurate targeting needles haystack, which “next-generation” materials.

Язык: Английский

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Selective Excitation of IR-Inactive Modes via Vibrational Polaritons: Insights from Atomistic Simulations

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 5034 - 5042

Опубликована: Май 13, 2025

Vibrational polaritons, hybrid light-matter states formed between molecular vibrations and infrared (IR) cavity modes, provide a novel approach for modifying chemical reaction pathways energy transfer processes. For vibrational polaritons involving condensed-phase molecules, the short polariton lifetime raises debate over whether pumping may produce different effects on molecules compared to directly exciting in free space or under weak coupling. Here, liquid methane strong coupling, classical dynamics simulations show that upper (UP) by asymmetric bending mode of can sometimes selectively excite IR-inactive symmetric mode. This finding is validated when system described using both empirical force fields machine-learning potentials, also qualitative agreement with analytical theory rates based Fermi's golden rule calculations. Additionally, our study suggests polariton-induced modes reaches maximal efficiency UP has significant contributions from photons underscoring importance hybridization. As are generally inaccessible direct IR excitation, highlights unique role formation controlling vibrations. Since this process occurs after decays, it impact photochemistry time scale longer than lifetime, as observed experiments.

Язык: Английский

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MIL‐91(Al) to Boost Solid–Solid Conversion Reactions in Li‐Se Batteries

Energy & environment materials, Год журнала: 2025, Номер unknown

Опубликована: Май 21, 2025

Lithium‐Selenium (Li‐Se) batteries have emerged as one of the most promising candidates for next‐generation energy storage systems owing to superior electronic conductivity, impressive volumetric capacity, and enhanced compatibility with carbonate electrolyte selenium, comparable sulfur. Despite these advantages, development Li‐Se is impeded by several intrinsic challenges, including volume expansion during discharge process consequent sluggish reaction kinetics that undermine their electrochemical performance. In this study, MIL‐91(Al) used an electrode additive accelerate one‐step mutual solid–solid conversion between Se Li 2 in carbonate‐based electrolyte. By doing so, uncontrollable deposition effectively mitigated, enhancing performance system. Thus, use results reduced internal resistance faster Li‐ion transfer rate, analyzed SPEIS GITT. Ab initio calculations molecular dynamics simulations further reveal anchors closely situated dangling oxygens phosphonate group organic linker MIL‐91(Al), inducing relaxation Li‐Se‐Li angle stabilizing overall structure. Accordingly, MIL‐91(Al)‐containing cells demonstrate a high specific capacity approximately 530 mAh g −1 at 1C (675 mA ) after 100 cycles retaining 320 mAh/g even under current rate (20C) 200 cycles. This research underlines importance electrocatalyst/electroadsorbent materials enhance redox reactions Se, thus paving way high‐performance batteries.

Язык: Английский

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Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals

Chemical Science, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

We fine-tune machine learning interatomic potentials to accurately model molecular crystals at finite temperature with the inclusion of nuclear quantum effects.

Язык: Английский

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