Thermodynamics-Consistent Graph Neural Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Rittig, Jan G., Mitsos, Alexander |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
por: Meir, Sagi, et al.
Publicado: (2025)
por: Meir, Sagi, et al.
Publicado: (2025)
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
por: Hino, Kentaro, et al.
Publicado: (2024)
por: Hino, Kentaro, 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)
por: Burin, Alexander L.
Publicado: (2025)
Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
por: Liu, Wei, et al.
Publicado: (2025)
por: Liu, Wei, et al.
Publicado: (2025)
Thermal conductivity of aligned polymers with kinks
por: Parshin, Igor V., et al.
Publicado: (2026)
por: Parshin, Igor V., et al.
Publicado: (2026)
Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
por: Zubatyuk, Tetiana, et al.
Publicado: (2019)
por: Zubatyuk, Tetiana, et al.
Publicado: (2019)
Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
por: Tiwary, Pratyush, et al.
Publicado: (2024)
por: Tiwary, Pratyush, et al.
Publicado: (2024)
Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
por: Peng, Jiayu, et al.
Publicado: (2026)
por: Peng, Jiayu, et al.
Publicado: (2026)
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)
por: Yan, Hongyu, et al.
Publicado: (2024)
Collective excitations in Hydrogen across the pressure-induced transition from molecular to atomic fluid
por: Ilenkov, I. -M., et al.
Publicado: (2025)
por: Ilenkov, I. -M., et al.
Publicado: (2025)
Efficient, Equivariant Predictions of Distributed Charge Models
por: Boittier, Eric D., et al.
Publicado: (2026)
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)
por: Demchuk, Taras, et al.
Publicado: (2025)
Spin-Dependent Graph Neural Network Potential for Magnetic Materials
por: Yu, Hongyu, et al.
Publicado: (2022)
por: Yu, Hongyu, et al.
Publicado: (2022)
Graph Neural Networks Do Not Always Oversmooth
por: Epping, Bastian, et al.
Publicado: (2024)
por: Epping, Bastian, et al.
Publicado: (2024)
Neural network distillation of orbital dependent density functional theory
por: Medvidović, Matija, et al.
Publicado: (2024)
por: Medvidović, Matija, et al.
Publicado: (2024)
Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
por: Tang, Ling, et al.
Publicado: (2025)
por: Tang, Ling, et al.
Publicado: (2025)
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)
por: Wada, Naoki, et al.
Publicado: (2026)
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
por: Raja, Sanjeev, et al.
Publicado: (2024)
por: Raja, Sanjeev, et al.
Publicado: (2024)
From latent dynamics to meaningful representations
por: Wang, Dedi, et al.
Publicado: (2022)
por: Wang, Dedi, et al.
Publicado: (2022)
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
por: Skenderi, Geri, et al.
Publicado: (2026)
por: Skenderi, Geri, et al.
Publicado: (2026)
Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems
por: Slavin, V., et al.
Publicado: (2025)
por: Slavin, V., et al.
Publicado: (2025)
Electric Polarization from Many-Body Neural Network Ansatz
por: Li, Xiang, et al.
Publicado: (2023)
por: Li, Xiang, et al.
Publicado: (2023)
Natural Quantization of Neural Networks
por: Barney, Richard, et al.
Publicado: (2025)
por: Barney, Richard, et al.
Publicado: (2025)
Projector, Neural, and Tensor-Network Representations of $\mathbb{Z}_N$ Cluster and Dipolar-cluster SPT States
por: Lee, Seungho, et al.
Publicado: (2026)
por: Lee, Seungho, et al.
Publicado: (2026)
Parallel Learning by Multitasking Neural Networks
por: Agliari, Elena, et al.
Publicado: (2023)
por: Agliari, Elena, et al.
Publicado: (2023)
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
por: Husistein, Raphael T., et al.
Publicado: (2024)
por: Husistein, Raphael T., et al.
Publicado: (2024)
Exponentially Tilted Thermodynamic Maps (expTM): Predicting Phase Transitions Across Temperature, Pressure, and Chemical Potential
por: Lee, Suemin, et al.
Publicado: (2025)
por: Lee, Suemin, et al.
Publicado: (2025)
Formation of Representations in Neural Networks
por: Ziyin, Liu, et al.
Publicado: (2024)
por: Ziyin, Liu, et al.
Publicado: (2024)
Optimized Machine Learning Methods for Studying the Thermodynamic Behavior of Complex Spin Systems
por: Kapitan, Dmitrii, et al.
Publicado: (2025)
por: Kapitan, Dmitrii, et al.
Publicado: (2025)
Kramers rate for Brownian particles in excitable media with deformable double-well substrates
por: Dikande, Alain M.
Publicado: (2025)
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)
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)
por: Ghasempouri, Seyed Ehsan, et al.
Publicado: (2025)
Annealing for prediction of grand canonical crystal structures: Efficient implementation of n-body atomic interactions
por: Couzinie, Yannick, et al.
Publicado: (2023)
por: Couzinie, Yannick, et al.
Publicado: (2023)
Bayesian RG Flow in Neural Network Field Theories
por: Howard, Jessica N., et al.
Publicado: (2024)
por: Howard, Jessica N., et al.
Publicado: (2024)
Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware
por: Lakhdar-Hamina, Djamil, et al.
Publicado: (2025)
por: Lakhdar-Hamina, Djamil, et al.
Publicado: (2025)
A Dynamical Model of Neural Scaling Laws
por: Bordelon, Blake, et al.
Publicado: (2024)
por: Bordelon, Blake, et al.
Publicado: (2024)
Neural network backflow for ab-initio quantum chemistry
por: Liu, An-Jun, et al.
Publicado: (2024)
por: Liu, An-Jun, et al.
Publicado: (2024)
How Feature Learning Can Improve Neural Scaling Laws
por: Bordelon, Blake, et al.
Publicado: (2024)
por: Bordelon, Blake, et al.
Publicado: (2024)
Demolition and Reinforcement of Memories in Spin-Glass-like Neural Networks
por: Ventura, Enrico
Publicado: (2024)
por: Ventura, Enrico
Publicado: (2024)
Dynamical Mean-Field Theory of Self-Attention Neural Networks
por: Poc-López, Ángel, et al.
Publicado: (2024)
por: Poc-López, Ángel, et al.
Publicado: (2024)
Ejemplares similares
-
First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
por: Meir, Sagi, et al.
Publicado: (2025) -
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
por: Hino, Kentaro, 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) -
Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
por: Liu, Wei, et al.
Publicado: (2025) -
Thermal conductivity of aligned polymers with kinks
por: Parshin, Igor V., et al.
Publicado: (2026)