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General reactive element-based machine learning potentials for heterogeneous catalysis

2025-09-23 10:40:02 英文原文

作者：Hu, P.

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摘要

The provided text appears to be a summary or abstract of an academic paper or research article, possibly focused on computational chemistry and materials science, specifically dealing with the development and application of machine learning models for predicting interatomic potentials. Below is a structured summary of key points and details from the reference list: ### Key Points: 1. **Introduction and Background**: - The document discusses methods for modeling chemical systems using atom-centered symmetry functions. - It mentions the Spectral Neighbor Analysis Method (SNAP) and CUR matrix decomposition techniques for improving data analysis. 2. **Methodology**: - The use of machine learning, particularly neural networks, to predict interatomic potentials is highlighted. - A Python module named `pyscal` is used for structural analysis of atomic environments. - Computational tools like LAMMPS and the Atomic Simulation Environment (ASE) are mentioned for simulations. 3. **Computational Details**: - Ab initio calculations were performed using plane-wave basis sets with pseudopotentials, specifically noting methods by Kresse & Joubert. - Density functional theory (DFT) was used to model interactions in solids and surfaces, employing the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and DFT-D dispersion corrections. 4. **Data Analysis**: - The paper includes structural analysis using bond-orientational order parameters (Steinhardt's method). - Python libraries such as NumPy are utilized for array programming in computational tasks. 5. **Applications**: - The research covers a wide range of applications, including the study of catalytic reactions on metal surfaces and structural transitions induced by adsorbate molecules. - Specific examples include the influence of surface defects on catalytic activity and selectivity, as well as the evolution of catalyst structures under reaction conditions. ### References: - **Theoretical Foundations**: - Bartók et al. (2013) discuss representing chemical environments using symmetry functions. - Thompson et al. (2015) describe spectral neighbor analysis for generating quantum-accurate potentials. - **Software Tools**: - NumPy (Harris et al., 2020) is referenced for array programming and computational efficiency. - LAMMPS (Thompson et al., 2022) is highlighted as a flexible simulation tool for particle-based materials modeling. - **Computational Methods**: - Kresse & Joubert's method (1999) for projector augmented-wave (PAW) approach in DFT calculations. - Grimme et al. (2010) provide parameters for density functional dispersion correction (DFT-D). - **Structural Analysis**: - Steinhardt et al. (1983) introduce bond-orientational order parameters to analyze atomic structures. ### Conclusion: The document emphasizes the integration of advanced computational methods and machine learning techniques in materials science research, particularly focusing on developing accurate interatomic potential models for predicting complex chemical phenomena such as catalytic reactions at surfaces. The references provide a comprehensive list of theoretical frameworks, software tools, and computational methodologies used in this work.

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