Cover Image

An interpretable molecular descriptor for machine learning predictions in atmospheric science

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lind, Linus, Sandström, Hilda, Rinke, Patrick
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ATMOMACCS: An interpretable molecular descriptor for machine learning predictions in atmospheric science
by: Lind, Linus Emil Elias
Published: (2025)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2026)

Towards data-driven mass spectrometry in atmospheric science
by: Sandström, Hilda, et al.
Published: (2023)

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2025)

Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
by: Sandström, Hilda, et al.
Published: (2024)

Quantum-centric machine learning for molecular dynamics
by: Tao, Yanxian, et al.
Published: (2025)

Accurate molecular polarizabilities with coupled-cluster theory and machine learning
by: Wilkins, David M., et al.
Published: (2018)

Enhancing molecular dynamics with equivariant machine-learned densities
by: Bogojeski, Mihail, et al.
Published: (2026)

A critical assessment of bonding descriptors for predicting materials properties
by: Naik, Aakash Ashok, et al.
Published: (2026)

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations
by: Zhang, Bohan, et al.
Published: (2025)

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion
by: Pisal, Prajwal, et al.
Published: (2024)

Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components
by: Fichtelmann, Peter, et al.
Published: (2025)

On the interpretation of molecular photoexcitation with long and ultrashort laser pulses
by: Janoš, Jiří, et al.
Published: (2026)

Water structuring at stacked graphene interfaces unveiled by machine-learning molecular dynamics
by: Hou, Dianwei, et al.
Published: (2025)

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework
by: Delesma, Francisco A., et al.
Published: (2023)

Machine learning evaluation of structural descriptors for supercooled water
by: Yoshikawa, Kohei, et al.
Published: (2026)

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
by: Mao, Yun-Wen, et al.
Published: (2023)

Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
by: Fazel, Kamron, et al.
Published: (2025)

Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers
by: Shiota, Tomoya, et al.
Published: (2024)

Integer linear programming for unsupervised training set selection in molecular machine learning
by: Haeberle, Matthieu, et al.
Published: (2024)

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning
by: Venkatachalam, Anbu Selvam, 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)

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
by: Timmerman, Lucas R., et al.
Published: (2024)

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
by: Muuga, Kadri, et al.
Published: (2026)

Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations
by: Yuan, Eric C. -Y., et al.
Published: (2025)

Uncertainty in the era of machine learning for atomistic modeling
by: Grasselli, Federico, et al.
Published: (2025)

Reducing cross-sample prediction churn in scientific machine learning
by: Prastalo, Gordan, et al.
Published: (2026)

Introduction to machine learning potentials for atomistic simulations
by: Thiemann, Fabian L., et al.
Published: (2024)

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
by: Pisal, Prajwal, et al.
Published: (2026)

Hierarchical quantum embedding by machine learning for large molecular assemblies
by: Bensberg, Moritz, et al.
Published: (2025)

Universal rapid machine learning models for predicting unconvoluted and convoluted X-ray Absorption Spectra
by: Zhan, Fei, et al.
Published: (2026)

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
by: Wang, Menghang, et al.
Published: (2025)

Physics-informed machine learning for combustion: A review
by: Wu, Jiahao, et al.
Published: (2025)

Basic stability tests of machine learning potentials for molecular simulations in computational drug discovery
by: Ranasinghe, Kavindri, et al.
Published: (2025)

Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings
by: Schultz, Jonathan D., et al.
Published: (2025)

Topological descriptors for the electron density of inorganic solids
by: Szymanski, Nathan J., et al.
Published: (2025)

Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction
by: Han, Bowen, et al.
Published: (2025)

On the design space between molecular mechanics and machine learning force fields
by: Wang, Yuanqing, et al.
Published: (2024)

Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2025)