Guest Editorial: Special Topic on Software for Atomistic Machine Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Rupp, Matthias, Küçükbenli, Emine, Csányi, Gábor |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Transferability of datasets between Machine-Learning Interaction Potentials
por: Niblett, Samuel P., et al.
Publicado: (2024)
por: Niblett, Samuel P., et al.
Publicado: (2024)
LATTE: an atomic environment descriptor based on Cartesian tensor contractions
por: Pellegrini, Franco, et al.
Publicado: (2024)
por: Pellegrini, Franco, et al.
Publicado: (2024)
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
por: Baldwin, William J., et al.
Publicado: (2026)
por: Baldwin, William J., et al.
Publicado: (2026)
Computing solvation free energies of small molecules with experimental accuracy
por: Moore, J. Harry, et al.
Publicado: (2024)
por: Moore, J. Harry, et al.
Publicado: (2024)
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
por: Kuryla, Domantas, et al.
Publicado: (2025)
por: Kuryla, Domantas, et al.
Publicado: (2025)
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
por: Pracht, Philipp, et al.
Publicado: (2024)
por: Pracht, Philipp, et al.
Publicado: (2024)
Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
por: Fu, Cong, et al.
Publicado: (2025)
por: Fu, Cong, et al.
Publicado: (2025)
Random Spin Committee Approach For Smooth Interatomic Potentials
por: Cărare, Vlad, et al.
Publicado: (2024)
por: Cărare, Vlad, 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)
por: Mathews, Samuel, et al.
Publicado: (2026)
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
por: Parker, Isaac J., et al.
Publicado: (2026)
por: Parker, Isaac J., et al.
Publicado: (2026)
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
por: Warford, Thomas, et al.
Publicado: (2026)
por: Warford, Thomas, et al.
Publicado: (2026)
Robustness of Local Predictions in Atomistic Machine Learning Models
por: Chong, Sanggyu, et al.
Publicado: (2023)
por: Chong, Sanggyu, et al.
Publicado: (2023)
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
por: Midgley, Laurence I., et al.
Publicado: (2026)
por: Midgley, Laurence I., et al.
Publicado: (2026)
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
por: Kovács, Dávid Péter, et al.
Publicado: (2023)
por: Kovács, Dávid Péter, et al.
Publicado: (2023)
Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
por: Elsner, Jan, et al.
Publicado: (2025)
por: Elsner, Jan, 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)
por: Qaisrani, Muhammad Nawaz, et al.
Publicado: (2025)
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields
por: Batatia, Ilyes, et al.
Publicado: (2025)
por: Batatia, Ilyes, et al.
Publicado: (2025)
Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials
por: Omranpour, Amir, et al.
Publicado: (2024)
por: Omranpour, Amir, et al.
Publicado: (2024)
Regularity Priors for the Linear Atomic Cluster Expansion
por: Darby, James P., et al.
Publicado: (2026)
por: Darby, James P., et al.
Publicado: (2026)
Optimal Invariant Bases for Atomistic Machine Learning
por: Allen, Alice E. A., et al.
Publicado: (2025)
por: Allen, Alice E. A., et al.
Publicado: (2025)
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
por: Karimitari, Nima, et al.
Publicado: (2026)
por: Karimitari, Nima, et al.
Publicado: (2026)
Black-Box Uncertainty Estimation for Deep Learning Models in Atomistic Simulations
por: Fonea, Idan, et al.
Publicado: (2025)
por: Fonea, Idan, et al.
Publicado: (2025)
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning
por: Willimetz, Daniel, et al.
Publicado: (2025)
por: Willimetz, Daniel, et al.
Publicado: (2025)
Scalable Reactive Atomistic Dynamics with GAIA
por: Song, Suhwan, et al.
Publicado: (2025)
por: Song, Suhwan, et al.
Publicado: (2025)
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
por: Cao, Zhonglin, et al.
Publicado: (2025)
por: Cao, Zhonglin, et al.
Publicado: (2025)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
por: Stark, Wojciech G., et al.
Publicado: (2024)
por: Stark, Wojciech G., et al.
Publicado: (2024)
Foundation Models for Atomistic Simulation of Chemistry and Materials
por: Yuan, Eric C. -Y., et al.
Publicado: (2025)
por: Yuan, Eric C. -Y., et al.
Publicado: (2025)
Learning to Dock: Geometric Deep Learning for Predicting Supramolecular Host-Guest Complexes
por: Wang, Zidi, et al.
Publicado: (2026)
por: Wang, Zidi, et al.
Publicado: (2026)
Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
por: Kresse, Jakob J., et al.
Publicado: (2025)
por: Kresse, Jakob J., et al.
Publicado: (2025)
Harnessing AtomisticSkills for Agentic Atomistic Research
por: Deng, Bowen, et al.
Publicado: (2026)
por: Deng, Bowen, et al.
Publicado: (2026)
Zero Shot Molecular Generation via Similarity Kernels
por: Elijošius, Rokas, et al.
Publicado: (2024)
por: Elijošius, Rokas, et al.
Publicado: (2024)
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
por: Gawkowski, Mikołaj J., et al.
Publicado: (2025)
por: Gawkowski, Mikołaj J., et al.
Publicado: (2025)
Atomistic QM/Classical Modeling of Surface-Enhanced Infrared Absorption
por: Sodomaco, Sveva, et al.
Publicado: (2025)
por: Sodomaco, Sveva, et al.
Publicado: (2025)
Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water
por: O'Neill, Niamh, et al.
Publicado: (2025)
por: O'Neill, Niamh, et al.
Publicado: (2025)
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
por: Costa, Allan dos Santos, et al.
Publicado: (2024)
por: Costa, Allan dos Santos, et al.
Publicado: (2024)
Atomistic Descriptor Optimization Using Complementary Euclidean and Geodesic Distance Information
por: Iyer, Gopal R., et al.
Publicado: (2024)
por: Iyer, Gopal R., et al.
Publicado: (2024)
Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$
por: Sumaria, Vaidish, et al.
Publicado: (2024)
por: Sumaria, Vaidish, et al.
Publicado: (2024)
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
por: Brunken, Christoph, et al.
Publicado: (2026)
por: Brunken, Christoph, et al.
Publicado: (2026)
RNA Dynamics and Interactions Revealed through Atomistic Simulations
por: Languin-Cattoën, Olivier, et al.
Publicado: (2025)
por: Languin-Cattoën, Olivier, et al.
Publicado: (2025)
Hybrid Atomistic-Parametric Decoherence Model for Molecular Spin Qubits
por: Aruachan, Katy, et al.
Publicado: (2025)
por: Aruachan, Katy, et al.
Publicado: (2025)
Ejemplares similares
-
Transferability of datasets between Machine-Learning Interaction Potentials
por: Niblett, Samuel P., et al.
Publicado: (2024) -
LATTE: an atomic environment descriptor based on Cartesian tensor contractions
por: Pellegrini, Franco, et al.
Publicado: (2024) -
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
por: Baldwin, William J., et al.
Publicado: (2026) -
Computing solvation free energies of small molecules with experimental accuracy
por: Moore, J. Harry, et al.
Publicado: (2024) -
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
por: Kuryla, Domantas, et al.
Publicado: (2025)