:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Tao, Shuo, Shao, Xuecheng, Zhu, Li
Format:	Preprint
Published:	2024
Subjects:	Materials Science
Online Access:	https://arxiv.org/abs/2401.13953
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties
by: Tao, Shuo, et al.
Published: (2024)

Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
by: Tao, Shuo, et al.
Published: (2026)

Nonlocal vs Local Pseudopotentials Affect Kinetic Energy Kernels in Orbital-Free DFT
by: Moldabekov, Zhandos A., et al.
Published: (2024)

Accelerating Moment Tensor Potentials through Post-Training Pruning
by: Meng, Zijian, et al.
Published: (2025)

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching
by: Zhu, Bonan, et al.
Published: (2021)

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
by: Nong, Wei, et al.
Published: (2025)

Approximate Excited-State Potential Energy Surfaces for Defects in Solids
by: Turiansky, Mark E., et al.
Published: (2025)

Anharmonic Terms of the Potential Energy Surface: A Group Theoretical Approach
by: Mitoli, Davide, et al.
Published: (2023)

A Foundational Potential Energy Surface Dataset for Materials
by: Kaplan, Aaron D., et al.
Published: (2025)

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
by: Chen, Siqi, et al.
Published: (2024)

Coupled Real- and Momentum-Space Topology in Symmetry-Locked Bilayer Altermagnet
by: Zhang, Shuo, et al.
Published: (2026)

Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows
by: Chawla, Vivek, et al.
Published: (2025)

Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
by: Shiota, Tomoya, et al.
Published: (2024)

NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
by: Fan, Zheyong, et al.
Published: (2026)

Accelerating Crystal Structure Prediction Using Data-Derived Potentials: High-Pressure Binary Hydrides
by: Conway, Lewis J., et al.
Published: (2025)

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
by: Gong, Xuhe, et al.
Published: (2025)

Termination-Dependent Surface States and Magnetic Fingerprints of Chiral Helimagnet Cr1/3TaS2
by: Liang, Bo, et al.
Published: (2026)

Accelerated Multi-Objective Alloy Discovery through Efficient Bayesian Methods: Application to the FCC Alloy Space
by: Hastings, Trevor, et al.
Published: (2024)

Ultrafast Magneto-optical Fingerprints of Altermagnetism in MnTe
by: Yang, Xu, et al.
Published: (2026)

Integrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves
by: Xu, Guanping, et al.
Published: (2025)

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
by: An, Yuqi, et al.
Published: (2026)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
by: Jia, Shuyi, et al.
Published: (2025)

Decoding the Electronic and Structural Fingerprints of Single-Atom Catalysts via DFT-Assisted XANES Analysis
by: Lazar, Petr, et al.
Published: (2026)

Unlocking the Power of Orbital-Free Density Functional Theory to Explore the Electronic Structure Under Extreme Conditions
by: Ma, Cheng, et al.
Published: (2026)

Systematic study of one-point kinetic energy density functionals for atomic nuclei
by: Shang, Tian Shuai, et al.
Published: (2026)

Pseudopotentials for Orbital-Free DFT: Capturing Nonlocality and Correcting Functional Approximants
by: Rios-Vargas, Valeria, et al.
Published: (2025)

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

A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
by: Meng, Zijian, et al.
Published: (2025)

Optimization of Coulomb Energies in Gigantic Configurational Spaces of Multi-Element Ionic Crystals
by: Köster, Konstantin, et al.
Published: (2024)

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)

Imposing Correct Jellium Response Is Key to Predict the Density Response by Orbital-Free DFT
by: Moldabekov, Zhandos A., et al.
Published: (2023)

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling
by: Zhu, Siya, et al.
Published: (2026)

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
by: Zhu, Siya, et al.
Published: (2024)

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
by: Neubert, Marlen, et al.
Published: (2025)

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
by: Lee, Huiju, et al.
Published: (2024)

Wannier Functions Dually Localized in Space and Energy
by: Mahler, Aaron, et al.
Published: (2022)

Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
by: Wang, Lin-Lin, et al.
Published: (2025)

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery
by: Cao, Bin, et al.
Published: (2026)

Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials
by: Krondorfer, Johannes K., et al.
Published: (2025)

How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
by: Shen, Chen, et al.
Published: (2026)