A Deep Learning Potential for Accurate Shock Response Simulations in Tin
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Yixin, Wang, Xiaoyang, Li, Wanghui, Chen, Mohan, Wang, Han |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
by: Wang, Yinan, et al.
Published: (2025)
by: Wang, Yinan, et al.
Published: (2025)
Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction
by: Gonzalez-Zapata, Simon, et al.
Published: (2026)
by: Gonzalez-Zapata, Simon, et al.
Published: (2026)
Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations
by: Matsumura, Naoki, et al.
Published: (2024)
by: Matsumura, Naoki, et al.
Published: (2024)
Multi-Source Domain Transfer Learning for Accurate Property Prediction in Two-Dimensional Materials
by: Zhang, Huiyang, et al.
Published: (2026)
by: Zhang, Huiyang, et al.
Published: (2026)
Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential
by: Hou, Enze, et al.
Published: (2025)
by: Hou, Enze, et al.
Published: (2025)
Deep Potentials for Materials Science
by: Wen, Tongqi, et al.
Published: (2022)
by: Wen, Tongqi, et al.
Published: (2022)
Symplectic Spin-Lattice Dynamics with Machine-Learning Potentials
by: Huang, Zhengtao, et al.
Published: (2025)
by: Huang, Zhengtao, et al.
Published: (2025)
Monolithic Germanium Tin on Si Avalanche Photodiodes
by: Rudie, Justin, et al.
Published: (2024)
by: Rudie, Justin, et al.
Published: (2024)
Machine-learned potential for amorphous Indium-Tin-Oxide alloys
by: Guo, Shuaiyang, et al.
Published: (2026)
by: Guo, Shuaiyang, et al.
Published: (2026)
Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
by: Wang, Haidi, et al.
Published: (2025)
by: Wang, Haidi, et al.
Published: (2025)
Symmetry-Driven Valleytronics in Single-Layer Tin Chalcogenides
by: Dien, Vo Khuong, et al.
Published: (2024)
by: Dien, Vo Khuong, et al.
Published: (2024)
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
by: Liu, Jianchuan, et al.
Published: (2023)
by: Liu, Jianchuan, et al.
Published: (2023)
An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments
by: Wu, Jintong, et al.
Published: (2025)
by: Wu, Jintong, et al.
Published: (2025)
A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)
by: Oh, Sangmin, et al.
Published: (2026)
Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark
by: Xiao, Enda, et al.
Published: (2025)
by: Xiao, Enda, et al.
Published: (2025)
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
by: Sharma, Abhishek, et al.
Published: (2024)
by: Sharma, Abhishek, et al.
Published: (2024)
Accurate and Efficient Interatomic Potentials for Dislocations in InP
by: Rocke, Thomas, et al.
Published: (2026)
by: Rocke, Thomas, et al.
Published: (2026)
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
by: Wang, Rui, et al.
Published: (2022)
by: Wang, Rui, et al.
Published: (2022)
Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces
by: Yang, Jing, et al.
Published: (2025)
by: Yang, Jing, et al.
Published: (2025)
Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net
by: Lee, Brian H., et al.
Published: (2026)
by: Lee, Brian H., et al.
Published: (2026)
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
by: Yang, Guang, et al.
Published: (2023)
by: Yang, Guang, et al.
Published: (2023)
Machine learning based nonlocal kinetic energy density functional for simple metals and alloys
by: Sun, Liang, et al.
Published: (2023)
by: Sun, Liang, et al.
Published: (2023)
Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors
by: Sun, Liang, et al.
Published: (2024)
by: Sun, Liang, et al.
Published: (2024)
Choosing Tight-Binding Models for Accurate Optoelectronic Responses
by: Ghosh, Andreas, et al.
Published: (2024)
by: Ghosh, Andreas, et al.
Published: (2024)
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
by: Zheng, Daye, et al.
Published: (2025)
by: Zheng, Daye, et al.
Published: (2025)
Direct Simulation of LiNi0.8Mn0.1Co0.1O2 Transport Properties Using an Efficient and Accurate Machine Learning Potential
by: He, Jian, et al.
Published: (2026)
by: He, Jian, et al.
Published: (2026)
A Fast, Accurate, and Reactive Equivariant Foundation Potential
by: Ko, Tsz Wai, et al.
Published: (2025)
by: Ko, Tsz Wai, et al.
Published: (2025)
Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning
by: Wang, Xiaoyang, et al.
Published: (2026)
by: Wang, Xiaoyang, et al.
Published: (2026)
Ultrafast Sliding Ferroelectric Switching in Bilayer Hexagonal Boron Nitride Revealed by Deep Learning Molecular Dynamics
by: Wang, Yinan, et al.
Published: (2026)
by: Wang, Yinan, et al.
Published: (2026)
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)
by: Birks, Fraser, et al.
Published: (2025)
Shock consolidation and the corresponding plasticity in nanopowdered Mg
by: He, D. B., et al.
Published: (2024)
by: He, D. B., et al.
Published: (2024)
A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
by: Gu, Yaohui, et al.
Published: (2026)
by: Gu, Yaohui, et al.
Published: (2026)
Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
by: Cha, Seungjun, et al.
Published: (2026)
by: Cha, Seungjun, et al.
Published: (2026)
Thermophysical and Mechanical Properties Prediction of Rear-earth High-entropy Pyrochlore Based on Deep-learning Potential
by: Wang, Yuxuan, et al.
Published: (2025)
by: Wang, Yuxuan, et al.
Published: (2025)
Single Crystalline Colloidal Quasi-Two-Dimensional Tin Telluride
by: Li, Fu, et al.
Published: (2020)
by: Li, Fu, et al.
Published: (2020)
Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
by: Shuang, Fei, et al.
Published: (2026)
by: Shuang, Fei, et al.
Published: (2026)
MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
by: Edwards, Connor W., et al.
Published: (2026)
by: Edwards, Connor W., et al.
Published: (2026)
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024)
by: Park, Yutack, et al.
Published: (2024)
Effects of Non-local Pseudopotentials on the Electrical and Thermal Transport Properties of Aluminum: A Density Functional Theory Study
by: Liu, Qianrui, et al.
Published: (2024)
by: Liu, Qianrui, et al.
Published: (2024)
Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density
by: Chen, Feng, et al.
Published: (2025)
by: Chen, Feng, et al.
Published: (2025)
Similar Items
-
OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
by: Wang, Yinan, et al.
Published: (2025) -
Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction
by: Gonzalez-Zapata, Simon, et al.
Published: (2026) -
Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations
by: Matsumura, Naoki, et al.
Published: (2024) -
Multi-Source Domain Transfer Learning for Accurate Property Prediction in Two-Dimensional Materials
by: Zhang, Huiyang, et al.
Published: (2026) -
Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential
by: Hou, Enze, et al.
Published: (2025)