Machine learning frontier orbital energies of nanodiamonds
Fuente:
arXiv
Saved in:
| Main Authors: | Kirschbaum, Thorren, von Seggern, Börries, Dzubiella, Joachim, Bande, Annika, Noé, Frank |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Transfer Learning for Molecular Property Predictions from Small Data Sets
by: Kirschbaum, Thorren, et al.
Published: (2024)
by: Kirschbaum, Thorren, et al.
Published: (2024)
Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)
by: Uhrin, Martin, et al.
Published: (2024)
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., et al.
Published: (2025)
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
Considerations in the use of ML interaction potentials for free energy calculations
by: Mendible, Orlando A., et al.
Published: (2024)
by: Mendible, Orlando A., et al.
Published: (2024)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Reducing cross-sample prediction churn in scientific machine learning
by: Prastalo, Gordan, et al.
Published: (2026)
by: Prastalo, Gordan, et al.
Published: (2026)
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)
by: Maruf, Moin Uddin, et al.
Published: (2025)
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., et al.
Published: (2025)
Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)
by: Zhang, Tianren, et al.
Published: (2025)
Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
by: Meyer, Ralf, et al.
Published: (2024)
by: Meyer, Ralf, et al.
Published: (2024)
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)
Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
by: Lam, Terry C. W., et al.
Published: (2026)
by: Lam, Terry C. W., et al.
Published: (2026)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, et al.
Published: (2024)
by: Liu, Junlan, et al.
Published: (2024)
Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
by: Ma, Andrew, et al.
Published: (2025)
by: Ma, Andrew, et al.
Published: (2025)
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
by: Romiluyi, Oyinkansola
Published: (2026)
by: Romiluyi, Oyinkansola
Published: (2026)
Machine Learning Multiscale Interactions
by: Solé, Àlex, et al.
Published: (2026)
by: Solé, Àlex, et al.
Published: (2026)
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)
Polarizable atomic multipoles for learning long-range electrostatics
by: Kim, Dongjin, et al.
Published: (2026)
by: Kim, Dongjin, et al.
Published: (2026)
Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
by: Kreiman, Tobias, et al.
Published: (2025)
by: Kreiman, Tobias, et al.
Published: (2025)
Latent Ewald summation for machine learning of long-range interactions
by: Cheng, Bingqing
Published: (2024)
by: Cheng, Bingqing
Published: (2024)
Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
by: Kanungo, Bikash, et al.
Published: (2024)
by: Kanungo, Bikash, et al.
Published: (2024)
Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
by: Willow, Soohaeng Yoo, et al.
Published: (2025)
by: Willow, Soohaeng Yoo, et al.
Published: (2025)
MBD-ML: Many-body dispersion from machine learning for molecules and materials
by: Moerman, Evgeny, et al.
Published: (2026)
by: Moerman, Evgeny, et al.
Published: (2026)
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Grad DFT: a software library for machine learning enhanced density functional theory
by: Casares, Pablo A. M., et al.
Published: (2023)
by: Casares, Pablo A. M., et al.
Published: (2023)
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
by: Amin, Ishan, et al.
Published: (2025)
by: Amin, Ishan, et al.
Published: (2025)
Delta-learned force fields for nonbonded interactions: Addressing the strength mismatch between covalent-nonbonded interaction for global models
by: Cázares-Trejo, Leonardo, et al.
Published: (2025)
by: Cázares-Trejo, Leonardo, et al.
Published: (2025)
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by: Zimmermann, Yoel, et al.
Published: (2024)
by: Zimmermann, Yoel, et al.
Published: (2024)
Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
by: Star, et al.
Published: (2024)
by: Star, et al.
Published: (2024)
Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
by: Sriram, Anuroop, et al.
Published: (2025)
by: Sriram, Anuroop, et al.
Published: (2025)
Transferable Learning of Reaction Pathways from Geometric Priors
by: Nam, Juno, et al.
Published: (2025)
by: Nam, Juno, et al.
Published: (2025)
Manifold Diffusion for Structure Generation of Transition Metal Complexes
by: Schaufelberger, Luca, et al.
Published: (2026)
by: Schaufelberger, Luca, et al.
Published: (2026)
A chemical language model for reticular materials design
by: Menon, Dhruv, et al.
Published: (2026)
by: Menon, Dhruv, et al.
Published: (2026)
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
by: Liu, Yutong, et al.
Published: (2025)
by: Liu, Yutong, et al.
Published: (2025)
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
by: Clausen, Christian M., et al.
Published: (2024)
by: Clausen, Christian M., et al.
Published: (2024)
XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
by: Grizzi, Vitor F., et al.
Published: (2026)
by: Grizzi, Vitor F., et al.
Published: (2026)
Similar Items
-
Transfer Learning for Molecular Property Predictions from Small Data Sets
by: Kirschbaum, Thorren, et al.
Published: (2024) -
Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024) -
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
by: Kempen, Luuk H. E., et al.
Published: (2025) -
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025) -
Considerations in the use of ML interaction potentials for free energy calculations
by: Mendible, Orlando A., et al.
Published: (2024)