:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Chengbing, Li, Yutong, Zhao, Rui, Liu, Zhoulin, Fan, Zheyong, Tang, Gang, Wang, Zhiyong
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Materials Science
Online Access:	https://arxiv.org/abs/2506.01868
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nepal Quality of Life (NepQol)
by: Population and Statistics Research Hub
Published: (2025)

Achieving Empirical Potential Efficiency with DFT Accuracy: A Neuroevolution Potential for the $α$-Fe--C--H System
by: Meng, Fan-Shun, et al.
Published: (2025)

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP
by: Yan, Zihan, et al.
Published: (2026)

Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients
by: Huang, Hongfu, et al.
Published: (2025)

The Role Of Ecce In Nep 2020 NEP 2020 and Early Childhood Care and Education (ECCE)
by: Kusmade, Mr. Akash Ramesh, et al.
Published: (2025)

NepTam: A Nepali-Tamang Parallel Corpus and Baseline Machine Translation Experiments
by: Ghimire, Rupak Raj, et al.
Published: (2026)

Role of Swami Vivekananda's Educational Philosophy in Promoting Peace and Moral Values Under Nep 2020
by: Amitesh Sarkar
Published: (2026)

NepEMO: A Multi-Label Emotion and Sentiment Analysis on Nepali Reddit with Linguistic Insights and Temporal Trends
by: Sitoula, Sameer, et al.
Published: (2025)

Hyperparameter Optimization and Force Error Correction of Neuroevolution Potential for Predicting Thermal Conductivity of Wurtzite GaN
by: Chen, Zhuo, et al.
Published: (2025)

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
by: Shuang, Fei, et al.
Published: (2026)

A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
by: Gu, Yaohui, et al.
Published: (2026)

Learning to shine: Neuroevolution enables optical control of phase transitions
by: Agrawal, Sraddha, et al.
Published: (2025)

Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials
by: Yan, Zihan, et al.
Published: (2025)

Understanding Magnesium Dissolution through Machine Learn-ing Molecular Dynamics
by: Liu, Zhoulin, et al.
Published: (2025)

Training of Technical Services Staff in the Automated Environment. SPEC Kit 171.
by: Bakke, Celia, et al.
Published: (1991)

Advances in modeling complex materials: The rise of neuroevolution potentials
by: Ying, Penghua, et al.
Published: (2025)

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
by: Lahouari, Adam, et al.
Published: (2025)

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
by: Fan, Zheyong, et al.
Published: (2026)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics
by: Li, Qing, et al.
Published: (2025)

Heat transport in superionic materials via machine-learned molecular dynamics
by: Zhou, Wenjiang, et al.
Published: (2025)

Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
by: Song, Keke, et al.
Published: (2024)

Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
by: Rybin, Nikita, et al.
Published: (2024)

Unveiling Mechanisms of SEI Formation and Sodium Loss in Sodium Batteries via Interface Reactor Sampling
by: Liu, Zhoulin, et al.
Published: (2026)

Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs
by: Zhou, Xiaoye, et al.
Published: (2024)

Nanozyme‐Activated PLLA Microspheres for Effective Tissue Detection, Treatment, and Restoration
by: Jianhong Cai, et al.
Published: (2025)

ORION: Unifying Top-Down and Bottom-Up Chemical Space Sampling for a Universal Organic Force Field
by: Chen, Zherui, et al.
Published: (2026)

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
by: Oerder, Rick, et al.
Published: (2026)

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
by: Fan, Zheyong, et al.
Published: (2023)

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)

Structure‐Dependent Nonlinear Optical Effects in Spiral WS2 Nanosheets
by: Junyuan Chen, et al.
Published: (2024)

Training‐Augmented Ionic Switch for Logic Signal Modulation
by: Rui Jia, et al.
Published: (2024)

Training-Free Active Learning Framework in Materials Science with Large Language Models
by: Wang, Hongchen, et al.
Published: (2025)

Enabling Self‐Adaptive Water‐Energy‐Balance of Photothermal Water Diode Evaporator: Dynamically Maximizing Energy Utilization Under the Ever‐Changing Sunlight
by: Dan Wei, et al.
Published: (2024)

Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
by: Liu, Jiahui, et al.
Published: (2024)

Interface phonon modes governing the ideal limit of thermal transport across diamond/cubic boron nitride interfaces
by: Wang, Xiaonan, et al.
Published: (2025)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications
by: In Kee Park, et al.
Published: (2026)

Tuning the lattice thermal conductivity in van-der-Waals structures through rotational (dis)ordering
by: Eriksson, Fredrik, et al.
Published: (2023)

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics
by: Žugec, Ivan, et al.
Published: (2025)

Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
by: Liu, Jiahui, et al.
Published: (2025)