Imagen de Portada

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zubatyuk, Tetiana, Nebgen, Ben, Lubbers, Nicholas, Smith, Justin S., Zubatyuk, Roman, Zhou, Guoqing, Koh, Christopher, Barros, Kipton, Isayev, Olexandr, Tretiak, Sergei
Formato:	Preprint
Publicado:	2019
Materias:	Disordered Systems and Neural Networks Chemical Physics Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
por: Li, Xinyang, et al.
Publicado: (2023)

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
por: Allen, Alice E. A., et al.
Publicado: (2025)

Linear-Scaling Potential-Free Data-Driven Molecular Dynamics for Arbitrary-Sized Water Clusters $(\text{H}_2\text{O})_n$
por: Yan, Hongyu, et al.
Publicado: (2024)

Super diffusive length dependent thermal conductivity in one-dimensional materials with structural defects: longitudinal to transverse phonon scattering leads to $κ\propto L^{1/3}$ law
por: Burin, Alexander L.
Publicado: (2025)

Thermal conductivity of aligned polymers with kinks
por: Parshin, Igor V., et al.
Publicado: (2026)

Collective excitations in Hydrogen across the pressure-induced transition from molecular to atomic fluid
por: Ilenkov, I. -M., et al.
Publicado: (2025)

Efficient, Equivariant Predictions of Distributed Charge Models
por: Boittier, Eric D., et al.
Publicado: (2026)

ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters
por: Demchuk, Taras, et al.
Publicado: (2025)

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
por: Liu, Wei, et al.
Publicado: (2025)

Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
por: Bensberg, Moritz, et al.
Publicado: (2025)

Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
por: Messerly, Mitchell, et al.
Publicado: (2025)

First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
por: Meir, Sagi, et al.
Publicado: (2025)

Kramers rate for Brownian particles in excitable media with deformable double-well substrates
por: Dikande, Alain M.
Publicado: (2025)

Transfer tensor analysis of localization in the Anderson and Aubry-André-Harper models
por: Anderson, Michelle C., et al.
Publicado: (2025)

Max-Cut graph-driven quantum circuit design for planar spin glasses
por: Ghasempouri, Seyed Ehsan, et al.
Publicado: (2025)

Neural network distillation of orbital dependent density functional theory
por: Medvidović, Matija, et al.
Publicado: (2024)

Annealing for prediction of grand canonical crystal structures: Efficient implementation of n-body atomic interactions
por: Couzinie, Yannick, et al.
Publicado: (2023)

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
por: Tang, Ling, et al.
Publicado: (2025)

Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study
por: Kobayashi, Keita, et al.
Publicado: (2024)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
por: Matin, Sakib, et al.
Publicado: (2025)

Disentangling morphology and conductance in amorphous graphene
por: Gastellu, Nicolas, et al.
Publicado: (2024)

Procedure for Obtaining the Analytical Distribution Function of Relaxation Times for the Analysis of Impedance Spectra using the Fox $H$-function
por: Allagui, Anis, et al.
Publicado: (2024)

Generalized Distribution Function of Relaxation Times with the Davidson-Cole Model as a Kernel
por: Allagui, Anis, et al.
Publicado: (2024)

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

A Global Spacetime Optimization Approach to the Real-Space Time-Dependent Schrödinger Equation
por: Hou, Enze, et al.
Publicado: (2025)

RiD-kit: Software package designed to do enhanced sampling using reinforced dynamics
por: Fan, Jiahao, et al.
Publicado: (2024)

Efficient optimization of neural network backflow for ab-initio quantum chemistry
por: Liu, An-Jun, et al.
Publicado: (2025)

Neural network backflow for ab-initio quantum chemistry
por: Liu, An-Jun, et al.
Publicado: (2024)

Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
por: Colglazier, William, et al.
Publicado: (2025)

Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
por: Sahrmann, Patrick G., et al.
Publicado: (2026)

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
por: Ibrahim, Akram, et al.
Publicado: (2023)

Slowly Quenched, High Pressure Glassy B$_2$O$_3$ at DFT Accuracy
por: Meher, Debendra, et al.
Publicado: (2024)

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
por: Wada, Naoki, et al.
Publicado: (2026)

Data-Driven Molecular Dynamics and TEM Analysis of Crystal Growth and Hydrogen Sensing in Pt-Functionalized Graphene Chemiresistive Sensors
por: Ibrahim, Akram, et al.
Publicado: (2025)

Orbital-interaction-aware deep learning model for efficient surface chemistry simulations
por: Zhang, Zhihao, et al.
Publicado: (2025)

Charge Hopping Dynamics along a Disordered Chain in Quantum Environments: Comparative Study of Different Rate Kernels
por: Jang, Seogjoo J., et al.
Publicado: (2026)

Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids
por: Vincely, Clement, et al.
Publicado: (2025)

Thermodynamics-Consistent Graph Neural Networks
por: Rittig, Jan G., et al.
Publicado: (2024)

On the Boroxol Ring Fraction in Melt-Quenched B$_2$O$_3$ Glass
por: Meher, Debendra, et al.
Publicado: (2025)

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
por: Raja, Sanjeev, et al.
Publicado: (2024)