Image de couverture de livre

Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tao, Shuo, Shao, Xuecheng, Zhu, Li
Format:	Preprint
Publié:	2024
Sujets:	Materials Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties
par: Tao, Shuo, et autres
Publié: (2024)

Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
par: Tao, Shuo, et autres
Publié: (2026)

Nonlocal vs Local Pseudopotentials Affect Kinetic Energy Kernels in Orbital-Free DFT
par: Moldabekov, Zhandos A., et autres
Publié: (2024)

Accelerating Moment Tensor Potentials through Post-Training Pruning
par: Meng, Zijian, et autres
Publié: (2025)

Approximate Excited-State Potential Energy Surfaces for Defects in Solids
par: Turiansky, Mark E., et autres
Publié: (2025)

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching
par: Zhu, Bonan, et autres
Publié: (2021)

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
par: Nong, Wei, et autres
Publié: (2025)

Anharmonic Terms of the Potential Energy Surface: A Group Theoretical Approach
par: Mitoli, Davide, et autres
Publié: (2023)

A Foundational Potential Energy Surface Dataset for Materials
par: Kaplan, Aaron D., et autres
Publié: (2025)

Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows
par: Chawla, Vivek, et autres
Publié: (2025)

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
par: Chen, Siqi, et autres
Publié: (2024)

Coupled Real- and Momentum-Space Topology in Symmetry-Locked Bilayer Altermagnet
par: Zhang, Shuo, et autres
Publié: (2026)

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

Accelerating Crystal Structure Prediction Using Data-Derived Potentials: High-Pressure Binary Hydrides
par: Conway, Lewis J., et autres
Publié: (2025)

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
par: Gong, Xuhe, et autres
Publié: (2025)

Accelerated Multi-Objective Alloy Discovery through Efficient Bayesian Methods: Application to the FCC Alloy Space
par: Hastings, Trevor, et autres
Publié: (2024)

Integrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves
par: Xu, Guanping, et autres
Publié: (2025)

Ultrafast Magneto-optical Fingerprints of Altermagnetism in MnTe
par: Yang, Xu, et autres
Publié: (2026)

Termination-Dependent Surface States and Magnetic Fingerprints of Chiral Helimagnet Cr1/3TaS2
par: Liang, Bo, et autres
Publié: (2026)

Decoding the Electronic and Structural Fingerprints of Single-Atom Catalysts via DFT-Assisted XANES Analysis
par: Lazar, Petr, et autres
Publié: (2026)

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
par: An, Yuqi, et autres
Publié: (2026)

NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
par: Fan, Zheyong, et autres
Publié: (2026)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
par: Jia, Shuyi, et autres
Publié: (2025)

A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
par: Meng, Zijian, et autres
Publié: (2025)

Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
par: Rybin, Nikita, et autres
Publié: (2024)

Optimization of Coulomb Energies in Gigantic Configurational Spaces of Multi-Element Ionic Crystals
par: Köster, Konstantin, et autres
Publié: (2024)

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling
par: Zhu, Siya, et autres
Publié: (2026)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
par: Kang, Kisung, et autres
Publié: (2024)

Pseudopotentials for Orbital-Free DFT: Capturing Nonlocality and Correcting Functional Approximants
par: Rios-Vargas, Valeria, et autres
Publié: (2025)

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
par: Lee, Huiju, et autres
Publié: (2024)

Wannier Functions Dually Localized in Space and Energy
par: Mahler, Aaron, et autres
Publié: (2022)

Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
par: Wang, Lin-Lin, et autres
Publié: (2025)

Unlocking the Power of Orbital-Free Density Functional Theory to Explore the Electronic Structure Under Extreme Conditions
par: Ma, Cheng, et autres
Publié: (2026)

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery
par: Cao, Bin, et autres
Publié: (2026)

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
par: Zhu, Siya, et autres
Publié: (2024)

How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
par: Shen, Chen, et autres
Publié: (2026)

Systematic study of one-point kinetic energy density functionals for atomic nuclei
par: Shang, Tian Shuai, et autres
Publié: (2026)

Accelerated Prediction of Surface Stability and Particle Morphology in Ionic Crystals via Electrostatic Screening
par: Baiju, Sourav, et autres
Publié: (2026)

Potential Energy Landscape as a Framework for Developing Innovative Materials
par: Andreeva, Nadezhda A., et autres
Publié: (2024)

Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon
par: Kohata, Ikuma, et autres
Publié: (2025)