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Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

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JOURNAL OF PHYSICS-CONDENSED MATTER 2025年第16期37卷 165802-165802页

作者： Tsuyama, Tomoyuki Kaneshita, Takeshi Matsui, Akira Ochiai, Kohei Tanaka, Hiroaki Kondo, Ryohei Fukushima, Takayuki Ohashi, Haruhisa Hashimoto, Atsushi Okuno, Yoshishige Zhu, Jian-Gang Resonac Corp Res Ctr Computat Sci & Informat 8 Ebisu ChoKanagawa Ku Yokohama Kanagawa 2218517 Japan Resonac Hard Disk Corp Res & Dev Ctr 5-1 Yawatakaigan Dori Ichihara Chiba 2900067 Japan Carnegie Mellon Univ Data Storage Syst Ctr Pittsburgh PA 15213 USA Carnegie Mellon Univ Elect & Comp Engn Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Mat Sci & Engn Dept Pittsburgh PA 15213 USA

We report the mechanisms of atomic ordering in Fe-Pt bimetallic alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phase, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at finite temperatures. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions. In contrast, the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order.

关键词： density functional theory atomic ordering machine learning interatomic potentials on-lattice Monte Carlo spin FePt

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Elemental augmentation of machine learning interatomic potentials

Computational Materials Today

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Computational Materials Today 2025年 6卷

作者： Haibo Xue Guanjian Cheng Wan-Jian Yin College of Energy Soochow Institute for Energy and Materials InnovationS (SIEMIS) and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies Soochow University Suzhou 215006 China

machine learning interatomic potentials (MLIPs) bridge the gap between the accuracy of ab initio methods and the computational efficiency needed for large-scale simulations. However, custom-trained MLIPs are often limited to specific materials and lack flexibility for incorporating additional elements, while universal potentials (UPots), despite covering a wide range of chemical elements, may sacrifice accuracy for generalization. In this work, we propose an elemental augmentation strategy to efficiently expand MLIPs by incorporating new elements into pre-trained models. Using a Bayesian optimization driven active learning framework, we target the configuration space of new elements where the current MLIPs exhibit high uncertainty and demonstrate the addition of up to 10 elements to a pre-trained UPot. The results demonstrate a high tendency for sampling new structures composed of these elements, minimizing sampling requirements. It reduces computational costs by over an order of magnitude compared to training an MLIP from scratch, while preserving accuracy. This strategy offers a scalable pathway to extend MLIP applicability across diverse chemical spaces.

关键词： machine learning interatomic potentials Elemental augmentation Potential energy surface Computational materials science

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Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by machine learning interatomic potentials

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ACS APPLIED ENERGY MATERIALS 2024年第1期8卷 492-502页

作者： Morrison, Oliver Uteva, Elena Walker, Gavin S. Grant, David M. Ling, Sanliang Univ Nottingham Fac Engn Adv Mat Res Grp Nottingham NG7 2RD England Aria Sustainabil Ltd Nottingham NG12 4DG England

Magnesium hydride (MgH2) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material's limiting factor is the high dehydrogenation temperature (over 300 degrees C) and sluggish (de)hydrogenation kinetics when no catalyst is present, making it impractical for onboard applications. Catalysts and physical restructuring (e.g., through ball milling) have both shown kinetic improvements, without full theoretical understanding as to why. In this work, we developed a machine learning interatomic potential (MLP) for the Mg-H system, which was used to run long time scale molecular dynamics (MD) simulations of a thick magnesium hydride surface slab for up to 1 ns. Our MLP-based MD simulations reveal previously unreported behavior of subsurface molecular H2 formation and subsequent trapping in the subsurface layer of MgH2. This hindered diffusion of subsurface H2 offers a partial explanation on the slow dehydrogenation kinetics of MgH2. The kinetics will be improved if a catalyst obstructs subsurface formation and trapping of H2 or if the diffusion of subsurface H2 is improved through defects created by physical restructuring.

关键词： Magnesium Hydride Molecular Dynamics Simulations machine learning interatomic potentials Hydrogen Storage Density Functional Theory

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Thermal boundary conductance of metal-diamond interfaces predicted by machine learning interatomic potentials

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2024年 235卷

作者： Adnan, Khalid Zobaid Neupane, Mahesh R. Feng, Tianli Univ Utah Dept Mech Engn Salt Lake City UT 84112 USA DEVCOM Army Res Lab Army Res Directorate ARD Adelphi MD 20708 USA

Thermal boundary conductance (TBC) across metal-diamond interfaces plays a critical role in the thermal management of future diamond-based ultrawide bandgap semiconductor devices. Molecular dynamics is a sophisticated method to predict TBC but is limited by the lack of reliable potential describing metal-diamond interfaces. In this work, we report the development of machine learning interatomic potentials and the prediction of TBCs of several technologically promising metal-diamond interfaces using nonequilibrium molecular dynamics. The predicted TBCs of relaxed Al, Mo, Zr, and Au-diamond interfaces are approximately 284, 93, 30, and 40 MW/m(2)K, respectively, after quantum corrections. The dependence of TBCs on pressure is also studied. The corresponding thermal boundary resistances are equivalent to 0.83-mu m thick of Al, 1.5-mu m Mo, 0.73-mu m Zr, and 7.9-mu m Au, respectively. We find that the conventional simple models, such as the acoustic mismatch model and diffuse mismatch model, even including the full-band phonon dispersion from first principles, largely misestimate the TBC values because of their inability to include the interfacial structural and bonding details as well as inelastic transmission. The quantum-corrected TBC values for the metal-diamond interfaces correlate well with the quantum-corrected phonon specific heat of metals, instead of diamond. Additionally, it is found that the Debye temperature shows a stronger correlation with the TBC than the elastic modulus does. The low TBC values between metals and diamond need to be considered in future diamond-based semiconductor devices.

关键词： Diamond Interfacial thermal resistance Electronics Molecular dynamics machine learning interatomic potentials

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Polymers simulation using machine learning interatomic potentials

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POLYMER 2024年 308卷

作者： Long, Teng Li, Jia Wang, Chenlu Wang, Hua Cheng, Xiao Lu, Haifeng Zhang, Ying Zhou, Chuanjian Shandong Univ Sch Mat Sci & Engn Jingshi Rd 17923 Jinan Shandong Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Sch Wenhuaxi Rd 180 Weihai Shandong Peoples R China Shandong Univ Sch Chem & Chem Engn Shandanan Rd 27 Jinan Shandong Peoples R China Shandong Univ Engn Training Ctr Erhuandong Rd 12550 Jinan Shandong Peoples R China

Polymers are essential in a wide range of industrial and everyday applications due to their unique properties. However, traditional simulation methods such as molecular dynamics and density functional theory face limitations in accurately and efficiently capturing the complex behavior of polymer systems. machine learning interatomic potentials (MLIPs) have emerged as a promising solution, combining the accuracy of quantum mechanical methods with the computational efficiency of classical molecular dynamics. This review provides a comprehensive overview of the advancements in MLIPs for polymer simulations, highlighting their application in predicting structural, dynamic, thermodynamic, and mechanical properties of certain polymers. We discuss the key challenges, including the complex polymer structures, the efficient collection of high-quality datasets, and the interpretability of the potential functions. Furthermore, we explore potential solutions, such as using advanced descriptors, active learning techniques, and improving model interpretability. Finally, we outline future research directions to enhance the applicability of MLIPs in polymer science, aiming to bridge the gap between theoretical simulations and real-world polymer applications.

关键词： machine learning interatomic potentials Polymer simulations Simulation stability

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Accessing negative Poisson's ratio of graphene by machine learning interatomic potentials

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NANOTECHNOLOGY 2022年第27期33卷 275710-275710页

作者： Wu, Jing Zhou, E. Qin, Zhenzhen Zhang, Xiaoliang Qin, Guangzhao Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Zhengzhou Univ Sch Phys & Microelect Int Lab Quantum Funct Mat Henan Zhengzhou 450001 Peoples R China Dalian Univ Technol Sch Energy & Power Engn Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China

The negative Poisson's ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials.

关键词： negative Poisson's ratio graphene machine learning interatomic potentials

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Benchmarking machine learning interatomic potentials via phonon anharmonicity

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machine learning-SCIENCE AND TECHNOLOGY 2024年第3期5卷 030502-030502页

作者： Bandi, Sasaank Jiang, Chao Marianetti, Chris A. Columbia Univ Dept Appl Phys & Appl Math New York NY 10027 USA Idaho Natl Lab Computat Mech & Mat Dept Idaho Falls ID 83415 USA

machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

关键词： phonon interactions thermal conductivity machine learning interatomic potentials

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Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2025年 244卷

作者： Luo, Wenzhu Yin, Ershuai Wang, Lei Lian, Wenlei Wang, Neng Li, Qiang Nanjing Univ Sci & Technol Sch Energy & Power Engn MIIT Key Lab Thermal Control Elect Equipment Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China Minist Ind & Informat Technol Key Lab Thermal Management & Energy Utilizat Aviat Nanjing 210016 Jiangsu Peoples R China

Heat transfer enhancement of N-Ga-Al semiconductor heterostructure interfaces is critical for the heat dissipation in GaN-based electronic devices, while the effect of the AlxGa(1-x)N transition layer component concentration and thickness on the heat transfer mechanism at the GaN-AlN interface is unclear. In this paper, using molecular dynamics simulations based on machine learning potentials, the interfacial thermal conductance (ITC) between GaN-AlxGa(1-x)N, AlN-AlxGa(1-x)N and GaN-AlxGa(1-x)N-AlN heterostructure interfaces are calculated for different transition layer thicknesses with different concentrations of Al fractions, and the reasons for the change of ITC and its heat transfer mechanism were explained by the phonon density of states and the spectral heat current. GaN-AlN heterostructure ITC at 300 K is calculated to be 557 MW/(m2K), and the ITCs of GaN-Al0.5Ga0.5 N and AlN-Al0.5Ga0.5 N are improved by 128 % and 229 % compared to GaN-AlN, whereas the ITCs of GaN-Al0.7Ga0.3N- AlN containing a 0.5 nm transition layer improved by 27.6 %. This is because elemental doping enhances phonon scattering near the interface thereby promoting phonon energy redistribution, but the bulk thermal resistance of the AlxGa(1-x)N layer also increases rapidly with increasing doping ratio, and ITC is affected by a combination of these two factors. This work aims to understand the mechanism of transition layer component concentration and thickness on the heat transfer at the GaN-AlN contact interface, which provides a useful guide for better thermal design of the GaN-AlN heterostructure interface.

关键词： GaN-AlN heterostructure interface Interfacial heat transfer machine learning interatomic potentials Molecular dynamics

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machine learning insights into calcium phosphate nucleation and aggregation

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ACTA BIOMATERIALIA 2025年 195卷 547-558页

作者： Wang, Jing Wang, Xin Xu, Dingguo Sichuan Univ Coll Chem MOE Key Lab Green Chem & Technol Chengdu 610064 Sichuan Peoples R China Sichuan Univ Res Ctr Mat Genome Engn Chengdu 610065 Sichuan Peoples R China

In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical component of bone and teeth. Our analysis encompassed the process from pre-nucleation stage to the growth of amorphous calcium phosphate (ACP) in solution. We observed fluctuations in free calcium ion concentration and tracked the formation of uniform clusters in the early nucleation phases, confirming the existence of pre-nucleation clusters (PNCs). The PNCs are characterized by the composition Ca2[(PO4)1.6(HPO4)(H2PO4)0.4] and predominantly exhibit a triangular structure formed by phosphate groups. This structure is not only the core of the short-range ordered units in ACP but also exhibits the structural characteristics of the fundamental building blocks of HAP. Importantly, these clusters interact dynamically with water molecules through hydrogen bonding and proton exchange, which is essential for their stability and growth. The gradual growth of these clusters occurs via ion attachment and cluster adsorption. This work provides insights into calcium phosphate mineralization, with implications for materials science and biomedical engineering, particularly in biomaterial synthesis. The application of MLIPs demonstrates a high-accuracy, efficient approach for simulating complex systems may advance our understanding of crystallization and biomineralization processes. Statement of significance: Calcium phosphate nucleation is crucial in biological mineralization and the synthesis of biomaterials, serving as a key aspect in the design of hydroxyapatite (HAP)-based biomaterials. However, the mechanisms of early nucleation remain unclear due to the complex ion-water interactions, which lead to rapid nucleation rates and small cluster sizes. This study combines MLIP with MD simulations to explore the nucleation process of calcium phosphate, revealing the transition from pre-nucleation to the formation of ACP. It clarifies t

关键词： machine learning interatomic potentials Calcium phosphate Nucleation process Pre-nucleation clusters

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Hydrogen Diffusion in Garnet: Insights From Atomistic Simulations

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GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS 2025年第2期26卷

作者： Zhong, Xin Hoefling, Felix John, Timm Free Univ Berlin Inst Geol Wissensch Berlin Germany Free Univ Berlin Fachbereich Math & Informat Berlin Germany Zuse Inst Berlin Berlin Germany

Garnet has been widely used to decipher the pressure-temperature-time history of rocks, but its physical properties such as elasticity and diffusion are strongly affected by trace amounts of hydrogen. Experimental measurements of H diffusion in garnet are limited to room pressure. We use atomistic simulations to study H diffusion in perfect and defective garnet lattices, focusing on protonation defects at the Si and Mg sites, which are shown to be energetically favored. Transient trapping of H renders ab-initio simulations of H diffusion computationally challenging, which is overcome with machine learning techniques by training a deep neural network that encodes the interatomic potential. Our results from such deep potential molecular dynamics (DeePMD) simulations show high mobility of hydrogen in defect-free garnet lattices, whereas H diffusivity is significantly diminished in defective lattices. Tracer simulations focusing on H alone highlight the vital role of atomic vibrations of heavier atoms like Mg on the release of H atoms. Two regimes of H diffusion are identified: a diffuser-dominated regime at high hydrogen content with low activation energies due to saturation of vacancies by hydrogen, and a vacancy-dominated regime at low hydrogen content with high activation energies due to trapping of H atoms at vacancy sites. These regimes account for experimental observations, such as a H-concentration dependent diffusivity and the discrepancy in activation energy between deprotonation and D-H exchange experiments. This study underpins the crucial role of vacancies in H diffusion and demonstrates the utility of machine-learned interatomic potentials in studying kinetic processes in the Earth's interior.

关键词： garnet hydrogen diffusion lattice defects ab initio molecular dynamics machine learning interatomic potentials

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