Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
Fuente:
arXiv
Saved in:
| Main Authors: | Shiota, Tomoya, Ishihara, Kenji, Do, Tuan Minh, Mori, Toshio, Mizukami, Wataru |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)
by: Kuroda, Naoya, et al.
Published: (2026)
Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers
by: Shiota, Tomoya, et al.
Published: (2024)
by: Shiota, Tomoya, et al.
Published: (2024)
Optimizing adsorption configurations on alloy surfaces using Tensor Train Optimizer
by: Do, Tuan Minh, et al.
Published: (2025)
by: Do, Tuan Minh, 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)
by: Shiota, Tomoya, et al.
Published: (2024)
Integrating Classical and Quantum Software for Enhanced Simulation of Realistic Chemical Systems
by: Shiota, Tomoya, et al.
Published: (2025)
by: Shiota, Tomoya, et al.
Published: (2025)
SEAMM: A Simulation Environment for Atomistic and Molecular Modeling
by: Saxe, Paul, et al.
Published: (2025)
by: Saxe, Paul, et al.
Published: (2025)
Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
by: Elsner, Jan, et al.
Published: (2025)
by: Elsner, Jan, et al.
Published: (2025)
A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
by: Sakano, Michael N., et al.
Published: (2026)
by: Sakano, Michael N., et al.
Published: (2026)
Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition
by: Chaudhuri, Shayantan, et al.
Published: (2024)
by: Chaudhuri, Shayantan, et al.
Published: (2024)
Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)
by: Chong, Sanggyu, et al.
Published: (2023)
ADEPT-PolyGraphMT: Automated Molecular Simulation and Multi-Task Multi-Fidelity Machine Learning for Polymer Property Generation and Prediction
by: Alosious, Sobin, et al.
Published: (2026)
by: Alosious, Sobin, et al.
Published: (2026)
Uncertainty and Exploration of Deep Learning-based Atomistic Models for Screening Molten Salt Properties and Compositions
by: Lam, Stephen T., et al.
Published: (2024)
by: Lam, Stephen T., et al.
Published: (2024)
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
by: Gawkowski, Mikołaj J., et al.
Published: (2025)
by: Gawkowski, Mikołaj J., et al.
Published: (2025)
Atomistic catalyst polarization stemming hydrogen generation from CH4
by: Wang, Sanmei, et al.
Published: (2024)
by: Wang, Sanmei, et al.
Published: (2024)
Harnessing AtomisticSkills for Agentic Atomistic Research
by: Deng, Bowen, et al.
Published: (2026)
by: Deng, Bowen, et al.
Published: (2026)
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning
by: Willimetz, Daniel, et al.
Published: (2025)
by: Willimetz, Daniel, et al.
Published: (2025)
Decoupling Precipitation and Surface Complexation during Mn(II) Removal by Biochar via Experiments and Atomistic Simulations
by: Ngambia, Audrey, et al.
Published: (2026)
by: Ngambia, Audrey, et al.
Published: (2026)
Atomistic insights on prebiotic phosphorylation of methanol from Schreibersite (Fe2NiP) corrosion: ab-initio computational study
by: Pantaleone, Stefano, et al.
Published: (2025)
by: Pantaleone, Stefano, et al.
Published: (2025)
Atomistic evolution of active sites in multi-component heterogeneous catalysts
by: Owen, Cameron J., et al.
Published: (2024)
by: Owen, Cameron J., et al.
Published: (2024)
Atomistic understanding of hydrogen coverage on RuO2(110) surface under electrochemical conditions from ab initio statistical thermodynamics
by: Zhang, Lei, et al.
Published: (2024)
by: Zhang, Lei, et al.
Published: (2024)
Advancing Surface Chemistry with Large-Scale Ab-Initio Quantum Many-Body Simulations
by: Huang, Zigeng, et al.
Published: (2024)
by: Huang, Zigeng, et al.
Published: (2024)
Photoreforming of plastic waste into valuable products and hydrogen using a high-entropy oxynitride with distorted atomic-scale structure
by: Hai, Ho Truong Nam, et al.
Published: (2024)
by: Hai, Ho Truong Nam, et al.
Published: (2024)
PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations
by: Miyazaki, Yu, et al.
Published: (2026)
by: Miyazaki, Yu, et al.
Published: (2026)
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
by: Ko, Tsz Wai, et al.
Published: (2024)
by: Ko, Tsz Wai, et al.
Published: (2024)
Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
by: Judge, Edmund, et al.
Published: (2024)
by: Judge, Edmund, et al.
Published: (2024)
Foundation Models for Atomistic Simulation of Chemistry and Materials
by: Yuan, Eric C. -Y., et al.
Published: (2025)
by: Yuan, Eric C. -Y., et al.
Published: (2025)
Modelling and Simulation of an Alkaline Ni/Zn Cell
by: Schwab, Felix K., et al.
Published: (2025)
by: Schwab, Felix K., et al.
Published: (2025)
Chemical Foundation Model Guided Design of High Ionic Conductivity Electrolyte Formulations
by: Zohair, Murtaza, et al.
Published: (2025)
by: Zohair, Murtaza, et al.
Published: (2025)
Auxiliary-field quantum Monte Carlo method with quantum selected configuration interaction
by: Yoshida, Yuichiro, et al.
Published: (2025)
by: Yoshida, Yuichiro, et al.
Published: (2025)
MACE Foundation Models for Lattice Dynamics: A Benchmark Study on Double Halide Perovskites
by: Yang, Jack, et al.
Published: (2025)
by: Yang, Jack, et al.
Published: (2025)
Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations
by: Hänseroth, Jonas, et al.
Published: (2026)
by: Hänseroth, Jonas, et al.
Published: (2026)
A Fast, Accurate, and Reactive Equivariant Foundation Potential
by: Ko, Tsz Wai, et al.
Published: (2025)
by: Ko, Tsz Wai, et al.
Published: (2025)
Foundation Models for Discovery and Exploration in Chemical Space
by: Wadell, Alexius, et al.
Published: (2025)
by: Wadell, Alexius, et al.
Published: (2025)
Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multi-Output & Multi-Fidelity Machine Learning Approach
by: Ibrahim, Akram, et al.
Published: (2024)
by: Ibrahim, Akram, et al.
Published: (2024)
Foundations of the ionization potential condition for localized electron removal in density functional theory
by: Ohad, Guy, et al.
Published: (2025)
by: Ohad, Guy, et al.
Published: (2025)
Atomic Decompositions of Periodic Electronic-Structure Simulations
by: Zamok, Luna, et al.
Published: (2024)
by: Zamok, Luna, et al.
Published: (2024)
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
by: Ding, Rui, et al.
Published: (2024)
by: Ding, Rui, et al.
Published: (2024)
Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
by: Peng, Jiayu, et al.
Published: (2026)
by: Peng, Jiayu, et al.
Published: (2026)
Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics
by: Kavanagh, Seán R.
Published: (2024)
by: Kavanagh, Seán R.
Published: (2024)
Detection of Nanopores with the Scanning Ion Conductance Microscopy: A Simulation Study
by: Qiu, Yinghua, et al.
Published: (2024)
by: Qiu, Yinghua, et al.
Published: (2024)
Similar Items
-
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026) -
Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers
by: Shiota, Tomoya, et al.
Published: (2024) -
Optimizing adsorption configurations on alloy surfaces using Tensor Train Optimizer
by: Do, Tuan Minh, 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) -
Integrating Classical and Quantum Software for Enhanced Simulation of Realistic Chemical Systems
by: Shiota, Tomoya, et al.
Published: (2025)