A deep learning model for chemical shieldings in molecular organic solids including anisotropy
Fuente:
arXiv
Saved in:
| Main Authors: | Kellner, Matthias, Holmes, Jacob B., Rodriguez-Madrid, Ruben, Viscosi, Florian, Zhang, Yuxuan, Emsley, Lyndon, Ceriotti, Michele |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quantum-corrected NMR crystallography at scale
by: Kellner, Matthias, et al.
Published: (2026)
by: Kellner, Matthias, et al.
Published: (2026)
Uncertainty quantification by direct propagation of shallow ensembles
by: Kellner, Matthias, et al.
Published: (2024)
by: Kellner, Matthias, et al.
Published: (2024)
How to Train a Shallow Ensemble
by: Schäfer, Moritz, et al.
Published: (2026)
by: Schäfer, Moritz, et al.
Published: (2026)
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
by: Kellner, Matthias, et al.
Published: (2026)
by: Kellner, Matthias, et al.
Published: (2026)
Rapid Assignment of Chemical Shifts From Crystal Structures in Solid‐State NMR
by: Ruben Rodriguez‐Madrid, et al.
Published: (2026)
by: Ruben Rodriguez‐Madrid, et al.
Published: (2026)
A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)
by: How, Wei Bin, et al.
Published: (2025)
Prediction rigidities for data-driven chemistry
by: Chong, Sanggyu, et al.
Published: (2024)
by: Chong, Sanggyu, et al.
Published: (2024)
Smooth, exact rotational symmetrization for deep learning on point clouds
by: Pozdnyakov, Sergey N., et al.
Published: (2023)
by: Pozdnyakov, Sergey N., et al.
Published: (2023)
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2024)
by: Bigi, Filippo, et al.
Published: (2024)
Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025)
by: Domina, Michelangelo, et al.
Published: (2025)
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)
Accurate molecular polarizabilities with coupled-cluster theory and machine learning
by: Wilkins, David M., et al.
Published: (2018)
by: Wilkins, David M., et al.
Published: (2018)
FlashMD: long-stride, universal prediction of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
Mechanistic study of mixed lithium halides solid state electrolytes
by: Tisi, Davide, et al.
Published: (2025)
by: Tisi, Davide, et al.
Published: (2025)
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)
by: Mazitov, Arslan, et al.
Published: (2025)
Probing the effects of broken symmetries in machine learning
by: Langer, Marcel F., et al.
Published: (2024)
by: Langer, Marcel F., et al.
Published: (2024)
Ab initio thermodynamics of liquid and solid water
by: Cheng, Bingqing, et al.
Published: (2018)
by: Cheng, Bingqing, et al.
Published: (2018)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)
by: How, Wei Bin, et al.
Published: (2024)
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)
by: Bigi, Filippo, et al.
Published: (2026)
3DReact: Geometric deep learning for chemical reactions
by: van Gerwen, Puck, et al.
Published: (2023)
by: van Gerwen, Puck, et al.
Published: (2023)
Fast and flexible long-range models for atomistic machine learning
by: Loche, Philip, et al.
Published: (2024)
by: Loche, Philip, et al.
Published: (2024)
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
by: Suman, Divya, et al.
Published: (2025)
by: Suman, Divya, et al.
Published: (2025)
Accelerating molecular vibrational spectra simulations with a physically informed deep learning model
by: Chen, Yuzhuo, et al.
Published: (2024)
by: Chen, Yuzhuo, et al.
Published: (2024)
Atom-Density Representations for Machine Learning
by: Willatt, Michael J., et al.
Published: (2018)
by: Willatt, Michael J., et al.
Published: (2018)
Modeling the structural and thermal properties of loaded metal-organic frameworks. An interplay of quantum and anharmonic fluctuations
by: Kapil, Venkat, et al.
Published: (2019)
by: Kapil, Venkat, et al.
Published: (2019)
Interaction frames in solid-state NMR: A case study for chemical-shift-selective irradiation schemes
by: Chávez, Matías, et al.
Published: (2022)
by: Chávez, Matías, et al.
Published: (2022)
Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
by: Musil, Felix, et al.
Published: (2018)
by: Musil, Felix, et al.
Published: (2018)
Novel deep‐learning model for chemical process fault detection based on DCW transformer
by: Ying Xie, et al.
Published: (2024)
by: Ying Xie, et al.
Published: (2024)
A novel deep‐learning fault detection model of MSLR ‐transformer for chemical process
by: Ying Xie, et al.
Published: (2025)
by: Ying Xie, et al.
Published: (2025)
A deep‐learning model based on MFE ‐Transformer for chemical process fault detection
by: Ying Xie, et al.
Published: (2025)
by: Ying Xie, et al.
Published: (2025)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Learning Long-Range Representations with Equivariant Messages
by: Rumiantsev, Egor, et al.
Published: (2025)
by: Rumiantsev, Egor, et al.
Published: (2025)
High-quality, high-information datasets for universal atomistic machine learning
by: Malosso, Cesare, et al.
Published: (2026)
by: Malosso, Cesare, et al.
Published: (2026)
Path Integral Methods in Atomistic Modelling: An Introduction
by: Ceriotti, Michele, et al.
Published: (2026)
by: Ceriotti, Michele, et al.
Published: (2026)
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
by: Mazitov, Arslan, et al.
Published: (2025)
by: Mazitov, Arslan, et al.
Published: (2025)
Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework
by: Lu, Xinyu, et al.
Published: (2025)
by: Lu, Xinyu, et al.
Published: (2025)
i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
by: Litman, Yair, et al.
Published: (2024)
by: Litman, Yair, et al.
Published: (2024)
Completeness of Atomic Structure Representations
by: Nigam, Jigyasa, et al.
Published: (2023)
by: Nigam, Jigyasa, et al.
Published: (2023)
Similar Items
-
Quantum-corrected NMR crystallography at scale
by: Kellner, Matthias, et al.
Published: (2026) -
Uncertainty quantification by direct propagation of shallow ensembles
by: Kellner, Matthias, et al.
Published: (2024) -
How to Train a Shallow Ensemble
by: Schäfer, Moritz, et al.
Published: (2026) -
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
by: Kellner, Matthias, et al.
Published: (2026) -
Rapid Assignment of Chemical Shifts From Crystal Structures in Solid‐State NMR
by: Ruben Rodriguez‐Madrid, et al.
Published: (2026)