Imagen de Portada

Black-Box Uncertainty Estimation for Deep Learning Models in Atomistic Simulations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Fonea, Idan, Peles, Amir, Niv, Sivan, Gordon, Goren, Natan, Amir
Formato:	Preprint
Publicado:	2025
Materias:	Chemical Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Fast calculation of retarded potentials in multi-domain TDDFT
por: Shapira, Matan, et al.
Publicado: (2023)

Foundation Models for Atomistic Simulation of Chemistry and Materials
por: Yuan, Eric C. -Y., et al.
Publicado: (2025)

Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials
por: Omranpour, Amir, et al.
Publicado: (2024)

Uncertainty and Exploration of Deep Learning-based Atomistic Models for Screening Molten Salt Properties and Compositions
por: Lam, Stephen T., et al.
Publicado: (2024)

Macroscopic Spin-Orbit Interaction through Strong-Field Pumping of Inhomogeneously Aligned Molecular Ensemble
por: Zanzuri, Uriel, et al.
Publicado: (2026)

SEAMM: A Simulation Environment for Atomistic and Molecular Modeling
por: Saxe, Paul, et al.
Publicado: (2025)

RNA Dynamics and Interactions Revealed through Atomistic Simulations
por: Languin-Cattoën, Olivier, et al.
Publicado: (2025)

Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
por: Elsner, Jan, et al.
Publicado: (2025)

Chemifriction and Superlubricity: Friends or Foes?
por: Ying, Penghua, et al.
Publicado: (2024)

Electrochemical Electron Transfer: Key Concepts, Theories, and Parameterization via Atomistic Simulations
por: Zhang, Mengke, et al.
Publicado: (2025)

Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition
por: Chaudhuri, Shayantan, et al.
Publicado: (2024)

A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning
por: Willimetz, Daniel, et al.
Publicado: (2025)

Robustness of Local Predictions in Atomistic Machine Learning Models
por: Chong, Sanggyu, et al.
Publicado: (2023)

Guest Editorial: Special Topic on Software for Atomistic Machine Learning
por: Rupp, Matthias, et al.
Publicado: (2024)

Selective Excitation of IR-Inactive Modes via Vibrational Polaritons: Insights from Atomistic Simulations
por: Ji, Xinwei, et al.
Publicado: (2025)

Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
por: Shiota, Tomoya, et al.
Publicado: (2024)

Atomistic QM/Classical Modeling of Surface-Enhanced Infrared Absorption
por: Sodomaco, Sveva, et al.
Publicado: (2025)

Scalable Reactive Atomistic Dynamics with GAIA
por: Song, Suhwan, et al.
Publicado: (2025)

Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models
por: Qaisrani, Muhammad Nawaz, et al.
Publicado: (2025)

Hybrid Atomistic-Parametric Decoherence Model for Molecular Spin Qubits
por: Aruachan, Katy, et al.
Publicado: (2025)

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
por: Musil, Felix, et al.
Publicado: (2018)

Path Integral Methods in Atomistic Modelling: An Introduction
por: Ceriotti, Michele, et al.
Publicado: (2026)

A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
por: Sakano, Michael N., et al.
Publicado: (2026)

The Impact of Hydration Shell Inclusion and Chain Exclusion in the Efficacy of Reaction Coordinates for Homogeneous and Heterogeneous Ice Nucleation
por: Sinaeian, Kimia, et al.
Publicado: (2025)

Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
por: Kresse, Jakob J., et al.
Publicado: (2025)

Unraveling Exciton Trap Dynamics and Nonradiative Loss Pathways in Quantum Dots via Atomistic Simulations
por: Hou, Bokang, et al.
Publicado: (2025)

Tensor-SqRA: Modeling the Transition Rates of Interacting Molecular Systems in terms of Potential Energies
por: Sikorski, Alexander, et al.
Publicado: (2023)

Harnessing AtomisticSkills for Agentic Atomistic Research
por: Deng, Bowen, et al.
Publicado: (2026)

The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
por: Gawkowski, Mikołaj J., et al.
Publicado: (2025)

Providing Machine Learning Potentials with High Quality Uncertainty Estimates
por: Sumer, Zeynep, et al.
Publicado: (2025)

Atomistic Descriptor Optimization Using Complementary Euclidean and Geodesic Distance Information
por: Iyer, Gopal R., et al.
Publicado: (2024)

Atomistic Modeling of Methane and Carbon Dioxide Structure I Gas Hydrates Under Pressure: Guest Effects and Properties
por: Mathews, Samuel, et al.
Publicado: (2026)

Enhanced 133cs Triple-Quantum Excitation in Solid-State NMR of Cs-Bearing Zeolites
por: Vaisleib, Noy, et al.
Publicado: (2025)

Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential
por: Omranpour, Amir, et al.
Publicado: (2025)

Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
por: Vinchurkar, Tirtha, et al.
Publicado: (2024)

A Massively Scalable Ligand-Protein Dissociation Dynamic Database Derived from Atomistic Molecular Modelling
por: Li, Maodong, et al.
Publicado: (2026)

W4$Λ$: leveraging $Λ$ coupled cluster for accurate computational thermochemistry approaches
por: Semidalas, Emmanouil, et al.
Publicado: (2023)

Machine Learning Potentials for Heterogeneous Catalysis
por: Omranpour, Amir, et al.
Publicado: (2024)

Mixed Atomistic-Implicit Quantum/Classical Approach to Molecular Nanoplasmonics
por: Illobre, Pablo Grobas, et al.
Publicado: (2024)

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
por: Maheshwari, Shagun, et al.
Publicado: (2025)