DeePMD-kit v2: A software package for deep potential models

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(5)

Published: Aug. 1, 2023

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version offers numerous advanced features, such DeepPot-SE, attention-based hybrid descriptors, ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support customized operators, compression, non-von Neumann dynamics, improved usability, including documentation, compiled binary packages, graphical user interfaces, application programming interfaces. article presents an overview major highlighting its features technical details. Additionally, this comprehensive procedure conducting representative application, benchmarks accuracy efficiency different models, discusses ongoing developments.

Language: Английский

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How to validate machine-learned interatomic potentials

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 158(12)

Published: March 2, 2023

Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods, there arises a need for careful validation, particularly physically agnostic models-that is, potentials that extract nature atomic interactions from reference data. Here, we review basic principles behind ML and their validation atomic-scale material modeling. We discuss best practice in defining error metrics based on numerical performance, as well guided validation. give specific recommendations hope will be useful wider community, including those researchers who intend to use materials "off shelf."

Language: Английский

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A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 158(8)

Published: Feb. 2, 2023

Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range molecular systems, from liquids to materials. In this study, we explore the possibility combining computational efficiency DeePMD framework and demonstrated accuracy MB-pol data-driven, many-body potential train DNN for large-scale water across its phase diagram. We find that is able reliably reproduce results liquid water, but provides less accurate description vapor-liquid equilibrium properties. This shortcoming traced back inability correctly represent interactions. An attempt explicitly include information about effects new exhibits opposite performance, being properties, losing These suggest DeePMD-based are not "learn" and, consequently, interactions, which implies may limited ability predict properties state points included training process. The can still be exploited on data-driven potentials, thus enable large-scale, "chemically accurate" various with caveat target must been adequately sampled by reference order guarantee faithful representation associated

Language: Английский

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Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Materials Horizons, Journal Year: 2023, Volume and Issue: 10(6), P. 1956 - 1968

Published: Jan. 1, 2023

This minireview highlights the superiority of machine learning interatomic potentials over conventional empirical and density functional theory calculations for analysis mechanical failure responses.

Language: Английский

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MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

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

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Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

iScience, Journal Year: 2024, Volume and Issue: 27(5), P. 109673 - 109673

Published: April 4, 2024

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and relatively low accuracy classical large-scale molecular dynamics, facilitating more efficient precise simulations materials research design. In this review, current state four essential stages MLIP is discussed, including data generation methods, material structure descriptors, six unique machine algorithms, available software. Furthermore, applications various fields are investigated, notably phase-change memory materials, searching, properties predicting, pre-trained universal models. Eventually, future perspectives, consisting standard datasets, transferability, generalization, trade-off between complexity MLIPs, reported.

Language: Английский

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Spin-dependent graph neural network potential for magnetic materials

Physical review. B./Physical review. B, Journal Year: 2024, Volume and Issue: 109(14)

Published: April 26, 2024

The development of machine-learning interatomic potentials has immensely contributed to the accuracy simulations molecules and crystals. However, creating for magnetic systems that account both moments structural degrees freedom remains a challenge. This work introduces SpinGNN, spin-dependent potential approach employs graph neural network (GNN) describe systems. SpinGNN consists two types edge GNNs: Heisenberg GNN (HEGNN) spin-distance (SEGNN). HEGNN is tailored capture Heisenberg-type spin-lattice interactions, while SEGNN accurately models multibody high-order coupling. effectiveness demonstrated by its exceptional precision in fitting spin Hamiltonian complex Hamiltonians with great precision. Furthermore, it successfully subtle coupling $\mathrm{BiFe}{\mathrm{O}}_{3}$ performs large-scale dynamics simulations, predicting antiferromagnetic ground state, phase transition, domain-wall energy landscape high accuracy. Finally, we perform over one million atoms across GPUs parallel. Our study broadens scope systems, serving as foundation carrying out dynamic first-principle on such

Language: Английский

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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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ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 483 - 493

Published: Jan. 2, 2025

Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one the most interesting challenges contemporary computational chemistry. However, traditional QM methods are costly this application. Machine learning techniques have emerged as a powerful tool substituting methods. Universal interatomic potentials (UIPs) hold particular promise to deliver accurate results at fraction cost methods, but performance UIPs calculating frequencies remains hitherto unknown. Here we show that despite known excellent representative UIP ANI-1ccx thermochemical properties, it fails due original unfortunate choice activation function. Hence, recommend evaluating new on an additional important quality test. To remedy shortcomings ANI-1ccx, introduce its reformulation ANI-1ccx-gelu with GELU function, which capable IR reasonable accuracy (close B3LYP/6-31G*). We also our can be fine-tuned obtain very some specific more effort needed improve overall and capability fine-tuning. The will included part universal updatable AI-enhanced (UAIQM) platform available together usage fine-tuning tutorials in open-source MLatom https://github.com/dralgroup/mlatom. calculations performed via web browser https://XACScloud.com.

Language: Английский

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Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles

Journal of Chemical Theory and Computation, Journal Year: 2022, Volume and Issue: 18(11), P. 6851 - 6865

Published: Oct. 4, 2022

Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among most common methodologies in chemistry for photophysical photochemical investigations. This paper describes main features of these methods how they implemented Newton-X. It emphasizes newest developments, including zero-point-energy leakage correction, complex-valued potential energy surfaces, induced by incoherent light, machine-learning potentials, exciton multiple chromophores, supervised unsupervised machine learning techniques. interfaced with several third-party quantum-chemistry programs, spanning a broad electronic structure methods.

Language: Английский

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