Copertina

Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shimamura, Kohei, Akihide, Koura, Shimojo, Fuyuki
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Computational Physics Disordered Systems and Neural Networks
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Machine Learning Interatomic Potentials with Keras API
di: Rili, James Paolo
Pubblicazione: (2024)

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties
di: Chen, Ruoxia, et al.
Pubblicazione: (2025)

Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics
di: Ghaffari, Kimia, et al.
Pubblicazione: (2024)

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
di: Focassio, Bruno, et al.
Pubblicazione: (2024)

Unraveling the Crystallization Kinetics of the Ge$_2$Sb$_2$Te$_5$ Phase Change Compound with a Machine-Learned Interatomic Potential
di: Kheir, Omar Abou El, et al.
Pubblicazione: (2023)

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
di: Tang, Ling, et al.
Pubblicazione: (2025)

Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys
di: Wei, Zixiong, et al.
Pubblicazione: (2024)

Critical States of Fermions with ${\mathbb{Z}}_2$ Flux Disorder
di: Das, Hiranmay, et al.
Pubblicazione: (2025)

Predicting the Mpemba Effect Using Machine Learning
di: Amorim, Felipe, et al.
Pubblicazione: (2022)

Universal Spin Models are Universal Approximators in Machine Learning
di: Reinhart, Tobias, et al.
Pubblicazione: (2025)

Field theory of non-Hermitian disordered systems
di: Chen, Ze, et al.
Pubblicazione: (2024)

Exact Learning Dynamics of In-Context Learning in Linear Transformers and Its Application to Non-Linear Transformers
di: Mainali, Nischal, et al.
Pubblicazione: (2025)

Electron Model on Truchet Tiling: Extended-to-Localized Transitions, Mobility Edge, and Asymmetric Spectrum
di: Jeon, Junmo, et al.
Pubblicazione: (2025)

Internal Stresses as Origin of the Anomalous Low-Temperature Specific Heat in Glasses
di: Schirmacher, Walter, et al.
Pubblicazione: (2025)

Unified Description of Learning Dynamics in the Soft Committee Machine from Finite to Ultra-Wide Regimes
di: Afanah, Assem, et al.
Pubblicazione: (2025)

$L_0$ Regularization of Field-Aware Factorization Machine through Ising Model
di: Okamoto, Yasuharu
Pubblicazione: (2024)

Machine Learning of Topological Insulator and Anderson Insulator in One-Dimensional Extended Su-Schrieffer-Heeger Chain
di: Yin, Zhekai, et al.
Pubblicazione: (2026)

The Low-Temperature Electronic Specific Heats of Disordered Ag-Au Alloys, Revisited
di: Hinojosa-Romero, David, et al.
Pubblicazione: (2024)

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
di: Clarté, Lucas, et al.
Pubblicazione: (2024)

Memory Uncertainty Relation and Harmonic Memory in Random Recurrent Networks
di: Haruna, Taichi, et al.
Pubblicazione: (2026)

Unlearning regularization for Boltzmann Machines
di: Ventura, Enrico, et al.
Pubblicazione: (2023)

Unbound States and Mixed Bound--Unbound Phases in Near-Infinitely Deep Potentials
di: Cheng, Shujie, et al.
Pubblicazione: (2026)

Machine learning of phases and structures for model systems in physics
di: Bayo, Djenabou, et al.
Pubblicazione: (2024)

How to Train an Oscillator Ising Machine using Equilibrium Propagation
di: Gower, Alex
Pubblicazione: (2025)

Constructing the low-temperature phase diagram for the $2+p$-quantum spin glass using the nonperturbative renormalization group
di: Achitouv, Ixandra, et al.
Pubblicazione: (2025)

Fermionic Machine Learning
di: Gince, Jérémie, et al.
Pubblicazione: (2024)

Continuous Specialization Transition in the Soft Committee Machine with ReLU Activation
di: Afanah, Assem, et al.
Pubblicazione: (2026)

Power-law localization in one-dimensional systems with nonlinear disorder under fixed input conditions
di: Nguyen, Ba Phi, et al.
Pubblicazione: (2024)

Polaritonic Machine Learning for Graph-based Data Analysis
di: Wang, Yuan, et al.
Pubblicazione: (2025)

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning
di: Malenfant-Thuot, Olivier, et al.
Pubblicazione: (2024)

High-throughput development of flexible amorphous materials showing large anomalous Nernst effect via automatic annealing and thermoelectric imaging
di: Park, Sang J., et al.
Pubblicazione: (2025)

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
di: Ibrahim, Akram, et al.
Pubblicazione: (2023)

Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
di: Kalra, Dayal Singh, et al.
Pubblicazione: (2024)

Exploring Metallic-Insulating Transition and Thermodynamic Applications of Fibonacci Quasicrystals
di: Xu, He-Guang, et al.
Pubblicazione: (2024)

Hexatic Phase in Covalent Two-Dimensional Silver Iodide
di: Bui, Thuy An, et al.
Pubblicazione: (2025)

Dynamical Cavity Method for Hypergraphs and its Application to Quenches in the k-XOR-SAT Problem
di: Maier, Aude, et al.
Pubblicazione: (2024)

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra
di: Klinavičius, Tomas, et al.
Pubblicazione: (2024)

An Ultra-Low Power and Fast Ising Machine using Voltage-Controlled Magnetoresistive Random Access Memory
di: Li, Sai, et al.
Pubblicazione: (2025)

The effect of priors on Learning with Restricted Boltzmann Machines
di: Manzan, Gianluca, et al.
Pubblicazione: (2024)

High Proton Conductivity of HxWO3 at Intermediate Temperatures: Unlocking Its Application as a Mixed Ionic-Electronic Conductor
di: Matsuo, Rantaro, et al.
Pubblicazione: (2025)