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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Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(1), P. 100049 - 100049

Published: Jan. 19, 2024

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI's pivotal roles field organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies planning, accelerates catalyst discovery, and fuels material innovation so on. It seamlessly integrates data-driven algorithms with intuition to redefine As chemistry advances, it promises accelerated research, sustainability, innovative solutions chemistry's pressing challenges. The fusion poised shape field's future profoundly, offering new horizons precision efficiency. encapsulates transformation marking moment where data converge revolutionize world molecules.

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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Investigating the decomposition mechanism of DNAN/DNB cocrystal explosive under high temperature using ReaxFF/lg molecular dynamics simulations

Journal of Molecular Modeling, Journal Year: 2025, Volume and Issue: 31(2)

Published: Jan. 18, 2025

Language: Английский

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Kinetic modeling of CL-20 decomposition by a chemical reaction neural network

Journal of Analytical and Applied Pyrolysis, Journal Year: 2023, Volume and Issue: 169, P. 105860 - 105860

Published: Jan. 1, 2023

Language: Английский

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Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(13), P. 9984 - 9997

Published: Jan. 1, 2024

NNP models covering three typical C/H/N/O element HEMs were developed to capture the mechanical and decomposition properties of RDX, HMX CL-20. The trajectory is mainly divided into two stages: pyrolysis oxidation.

Language: Английский

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The thermal decomposition mechanism of RDX/AP composites: ab initio neural network MD simulations

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(15), P. 11545 - 11557

Published: Jan. 1, 2024

A neural network potential (NNP) is developed to investigate the decomposition mechanism of RDX, AP, and their composites.

Language: Английский

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Revealing the thermal decomposition mechanism of RDX crystals by a neural network potential

Physical Chemistry Chemical Physics, Journal Year: 2022, Volume and Issue: 24(42), P. 25885 - 25894

Published: Jan. 1, 2022

A neural network potential (NNP) is developed to investigate the complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX) thermal decomposition.

Language: Английский

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Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Fuel, Journal Year: 2023, Volume and Issue: 357, P. 129909 - 129909

Published: Sept. 23, 2023

Language: Английский

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Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis

Energetic Materials Frontiers, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 1, 2023

In this study, machine learning (ML)-assisted regression modeling was conducted to predict the thermal decomposition temperatures and explore factors that correlate with stability of energetic materials (EMs). The performed based on a dataset consisting 885 various compounds using linear nonlinear algorithms. tree-based models established demonstrated acceptable predictive abilities, yielding low mean absolute error (MAE) 31°C. By analyzing through hierarchical classification, study insightfully identified affecting EMs' temperatures, overall accuracy improved targeted modeling. SHapley Additive exPlanations (SHAP) analysis indicated descriptors such as BCUT2D, PEOE_VSA, MolLog_P, TPSA played significant role, demonstrating process is influenced by multiple relating composition, electron distribution, chemical bond properties, substituent type molecules. Additionally, Carbon_contents Oxygen_Balance proposed for characterizing EMs showed strong correlations temperatures. trends their SHAP values most suitable ranges were 0.2–0.35 −65 −55, respectively. Overall, shows potential ML temperature prediction provides insights into characteristics molecular descriptors.

Language: Английский

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