Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Tiefenbacher, Maximilian X., Bachmair, Brigitta, Chen, Cheng Giuseppe, Westermayr, Julia, Marquetand, Philipp, Dietschreit, Johannes C. B., González, Leticia |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics
by: Yang, Xiran, et al.
Published: (2026)
by: Yang, Xiran, et al.
Published: (2026)
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
by: Majumdar, Sauradeep, et al.
Published: (2026)
by: Majumdar, Sauradeep, et al.
Published: (2026)
Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
by: Barrett, Rhyan, et al.
Published: (2023)
by: Barrett, Rhyan, et al.
Published: (2023)
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
by: Barrett, Rhyan, et al.
Published: (2025)
by: Barrett, Rhyan, et al.
Published: (2025)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)
by: Stark, Wojciech G., et al.
Published: (2024)
Cartesian atomic cluster expansion for machine learning interatomic potentials
by: Cheng, Bingqing
Published: (2024)
by: Cheng, Bingqing
Published: (2024)
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components
by: Fichtelmann, Peter, et al.
Published: (2025)
by: Fichtelmann, Peter, et al.
Published: (2025)
Including photoexcitation explicitly in trajectory-based nonadiabatic dynamics at no cost
by: Janoš, Jiří, et al.
Published: (2024)
by: Janoš, Jiří, et al.
Published: (2024)
Transferable excited-state dynamics enable screening of fluorescent protein chromophores
by: Barrett, Rhyan, et al.
Published: (2026)
by: Barrett, Rhyan, et al.
Published: (2026)
Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)
by: Mehdi, Shams, et al.
Published: (2026)
Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)
by: Bigi, Filippo, et al.
Published: (2026)
Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
by: Vorotnikov, Igor, et al.
Published: (2025)
by: Vorotnikov, Igor, et al.
Published: (2025)
The Entropic Barrier around the Conical Intersection Seam
by: Dietschreit, Johannes C. B., et al.
Published: (2026)
by: Dietschreit, Johannes C. B., et al.
Published: (2026)
Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2026)
by: Bhatia, Nitik, et al.
Published: (2026)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations
by: Barrett, Rhyan, et al.
Published: (2025)
by: Barrett, Rhyan, et al.
Published: (2025)
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
by: Matin, Sakib, et al.
Published: (2025)
by: Matin, Sakib, et al.
Published: (2025)
Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite
by: Del Fré, Samuel, et al.
Published: (2026)
by: Del Fré, Samuel, et al.
Published: (2026)
Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
by: Brugnoli, Luca, et al.
Published: (2026)
by: Brugnoli, Luca, 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)
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)
by: Bhatia, Nitik, 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)
Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
by: Fazel, Kamron, et al.
Published: (2025)
by: Fazel, Kamron, et al.
Published: (2025)
Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
by: Kim, Minwoo, et al.
Published: (2026)
by: Kim, Minwoo, et al.
Published: (2026)
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
by: David, Rolf, et al.
Published: (2024)
by: David, Rolf, et al.
Published: (2024)
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)
by: Farris, Riccardo, et al.
Published: (2025)
Statistics makes a difference: Machine learning adsorption dynamics of functionalized cyclooctine on Si(001) at DFT accuracy
by: Weiske, Hendrik, et al.
Published: (2025)
by: Weiske, Hendrik, et al.
Published: (2025)
High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
by: Goswami, Shubhang, et al.
Published: (2024)
by: Goswami, Shubhang, et al.
Published: (2024)
Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
by: Ibragimova, Rina, et al.
Published: (2024)
by: Ibragimova, Rina, et al.
Published: (2024)
Predicting solvation free energies with an implicit solvent machine learning potential
by: Röcken, Sebastien, et al.
Published: (2024)
by: Röcken, Sebastien, et al.
Published: (2024)
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
by: Holzenkamp, Matthias, et al.
Published: (2024)
by: Holzenkamp, Matthias, et al.
Published: (2024)
Manipulating nonadiabatic dynamics by plasmonic nanocavity
by: Wang, Yu, et al.
Published: (2025)
by: Wang, Yu, et al.
Published: (2025)
Best practices for nonadiabatic molecular dynamics simulations
by: Prlj, Antonio, et al.
Published: (2025)
by: Prlj, Antonio, et al.
Published: (2025)
Coupled cluster theory for nonadiabatic dynamics: nuclear gradients and nonadiabatic couplings in similarity constrained coupled cluster theory
by: Kjønstad, Eirik F., et al.
Published: (2024)
by: Kjønstad, Eirik F., et al.
Published: (2024)
Machine learning interatomic potentials for lithium battery electrolyte design
by: Gunwook Nam, et al.
Published: (2026)
by: Gunwook Nam, et al.
Published: (2026)
Hierarchical generative modeling for the design of multi-component systems
by: Barrett, Rhyan, et al.
Published: (2026)
by: Barrett, Rhyan, et al.
Published: (2026)
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
Latent space design of interatomic potentials
by: Atlas, Susan R.
Published: (2026)
by: Atlas, Susan R.
Published: (2026)
Similar Items
-
NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics
by: Yang, Xiran, et al.
Published: (2026) -
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
by: Majumdar, Sauradeep, et al.
Published: (2026) -
Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
by: Barrett, Rhyan, et al.
Published: (2023) -
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
by: Barrett, Rhyan, et al.
Published: (2025) -
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)