Buchumschlag

Machine learning surrogate models for particle insertions and element substitutions

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Jinnouchi, Ryosuke
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Chemical Physics
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Machine Learning-Aided First-Principles Calculations of Redox Potentials
von: Jinnouchi, Ryosuke, et al.
Veröffentlicht: (2023)

Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
von: Jinnouchi, Ryosuke, et al.
Veröffentlicht: (2024)

Machine learning models for atom-diatom reactions across isotopologues
von: Julian, Daniel, et al.
Veröffentlicht: (2024)

Field theoretic atomistics: Learning thermodynamic and variational surrogate to density functional theory
von: Das, Sambit, et al.
Veröffentlicht: (2025)

Can machines learn density functionals? Past, present, and future of ML in DFT
von: Akashi, Ryosuke, et al.
Veröffentlicht: (2025)

Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
von: Suntaxi, Geremy Loachamín, et al.
Veröffentlicht: (2024)

Machine learning potentials for redox chemistry in solution
von: Kocer, Emir, et al.
Veröffentlicht: (2024)

Isomer effects on neutral-loss dissociation channels of nitrogen-substituted PAH dications
von: Srivastav, Sumit, et al.
Veröffentlicht: (2025)

Pathway selectivity in time-resolved spectroscopy using two-photon coincidence counting with quantum entangled photons
von: Fujihashi, Yuta, et al.
Veröffentlicht: (2023)

Excited state assignment and state-resolved photoelectron circular dichroism in chalcogen-substituted fenchones
von: Vasudevan, Sudheendran, et al.
Veröffentlicht: (2025)

Mimyria: Machine learned vibrational spectroscopy for aqueous systems made simple
von: Schienbein, Philipp
Veröffentlicht: (2026)

Machine learning meets $\mathfrak{su}(n)$ Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
von: Ullah, Arif, et al.
Veröffentlicht: (2025)

Duality of particle-hole and particle-particle theories for strongly correlated electronic systems
von: Tucholska, Aleksandra, et al.
Veröffentlicht: (2024)

Plasmon-driven substitution of 4 mercaptophenylboronic acid to 4-nitrothiophenol monitored by surface-enhanced Raman spectroscopy
von: Kozisek, Jan, et al.
Veröffentlicht: (2024)

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

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

Two-dimensional fluorescence spectroscopy with quantum entangled photons and time- and frequency-resolved two-photon coincidence detection
von: Fujihashi, Yuta, et al.
Veröffentlicht: (2025)

Machine learning electronic structure and atomistic properties from the external potential
von: Nigam, Jigyasa, et al.
Veröffentlicht: (2026)

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
von: Lüder, Johann, et al.
Veröffentlicht: (2024)

Free-particle Green's function matrix elements over spherical Gaussian and plane-wave-modulated Gaussian basis functions
von: Mahato, Dibyendu, et al.
Veröffentlicht: (2026)

Nucleophilic substitution at silicon under vibrational strong coupling: Refined insights from a high-level ab initio perspective
von: Frerick, Niels-Ole, et al.
Veröffentlicht: (2026)

Fast and flexible long-range models for atomistic machine learning
von: Loche, Philip, et al.
Veröffentlicht: (2024)

Active learning and explicit electrostatics enable accurate modeling of electrolytes
von: Chalykh, Olga, et al.
Veröffentlicht: (2025)

Consistent inclusion of triple substitutions within a coupled cluster based static quantum embedding theory
von: Shee, Avijit, et al.
Veröffentlicht: (2026)

Machine learning the two-electron reduced density matrix in molecules and condensed phases
von: B., Jessica A. Martinez, et al.
Veröffentlicht: (2026)

Machine learning prediction of a chemical reaction over 8 decades of energy
von: Julian, Daniel, et al.
Veröffentlicht: (2025)

Resolving the size and charge of small particles: a predictive model of nanopore mechanics
von: Bearden, Samuel, et al.
Veröffentlicht: (2024)

Artificial-intelligence-based surrogate solution of dissipative quantum dynamics: physics-informed reconstruction of the universal propagator
von: Zhang, Jiaji, et al.
Veröffentlicht: (2024)

Analytical modeling of coated plasmonic particles
von: Khlebtsov, Nikolai G., et al.
Veröffentlicht: (2024)

Machine learning for accuracy in density functional approximations
von: Voss, Johannes
Veröffentlicht: (2023)

Reactions dynamics for X + H2 insertion reactions(X=C(1D), N(2D), O(1D), S(1D)) with Cayley Propagator ring-polymer molecular dynamics
von: Jiang, Wenbin, et al.
Veröffentlicht: (2024)

Accelerating inverse materials design using generative diffusion models with reinforcement learning
von: Chen, Junwu, et al.
Veröffentlicht: (2025)

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy
von: Hänseroth, Jonas, et al.
Veröffentlicht: (2025)

A hybrid model of sulphation reactions: stochastic particles in a random continuum environment
von: Jävergård, Nicklas, et al.
Veröffentlicht: (2025)

Accelerating molecular vibrational spectra simulations with a physically informed deep learning model
von: Chen, Yuzhuo, et al.
Veröffentlicht: (2024)

A deep learning model for chemical shieldings in molecular organic solids including anisotropy
von: Kellner, Matthias, et al.
Veröffentlicht: (2025)

Machine-learning structural reconstructions for accelerated point defect calculations
von: Mosquera-Lois, Irea, et al.
Veröffentlicht: (2024)

Machine learning classification of local environments in molecular crystals
von: Kuroshima, Daisuke, et al.
Veröffentlicht: (2024)

Machine learning accelerated finite-field simulations for electrochemical interfaces
von: Feng, Chaoqiang, et al.
Veröffentlicht: (2025)

Carbon black and hydrogen production from methane pyrolysis: measured and modeled insights from integrated gas and particle diagnostics in shock tubes
von: Clark, Gibson, et al.
Veröffentlicht: (2026)