How big is Big Data?
Fuente:
arXiv
Saved in:
| Main Authors: | Speckhard, Daniel T., Bechtel, Tim, Ghiringhelli, Luca M., Kuban, Martin, Rigamonti, Santiago, Draxl, Claudia |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MADAS -- A Python framework for assessing similarity in materials-science data
by: Kuban, Martin, et al.
Published: (2024)
by: Kuban, Martin, et al.
Published: (2024)
Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
by: Speckhard, Daniel T., et al.
Published: (2025)
by: Speckhard, Daniel T., et al.
Published: (2025)
Cluster Expansion Toward Nonlinear Modeling and Classification
by: Stroth, Adrian, et al.
Published: (2025)
by: Stroth, Adrian, et al.
Published: (2025)
On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
by: Rensmeyer, Tim, et al.
Published: (2025)
by: Rensmeyer, Tim, et al.
Published: (2025)
Information-Theoretic Grid Topology Reconstruction using Low-Precision Smart Meter Data
by: Speckhard, Daniel T.
Published: (2025)
by: Speckhard, Daniel T.
Published: (2025)
When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential
by: Gibson, Jason B., et al.
Published: (2024)
by: Gibson, Jason B., et al.
Published: (2024)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis
by: Biswajeet, Devi Dutta, et al.
Published: (2025)
by: Biswajeet, Devi Dutta, et al.
Published: (2025)
Learning charges and long-range interactions from energies and forces
by: Kim, Dongjin, et al.
Published: (2024)
by: Kim, Dongjin, et al.
Published: (2024)
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
by: Elsborg, Jonas, et al.
Published: (2026)
by: Elsborg, Jonas, 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)
Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)
Polarizable atomic multipoles for learning long-range electrostatics
by: Kim, Dongjin, et al.
Published: (2026)
by: Kim, Dongjin, et al.
Published: (2026)
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)
by: Solé, Àlex, et al.
Published: (2025)
Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)
by: Uhrin, Martin, et al.
Published: (2024)
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction
by: Yin, Shi, et al.
Published: (2024)
by: Yin, Shi, et al.
Published: (2024)
The Northeast Materials Database for Magnetic Materials
by: Itani, Suman, et al.
Published: (2024)
by: Itani, Suman, et al.
Published: (2024)
A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects
by: Yu, Hao
Published: (2024)
by: Yu, Hao
Published: (2024)
Response Matching for generating materials and molecules
by: Cheng, Bingqing
Published: (2024)
by: Cheng, Bingqing
Published: (2024)
EPi-cKANs: Elasto-Plasticity Informed Kolmogorov-Arnold Networks Using Chebyshev Polynomials
by: Mostajeran, Farinaz, et al.
Published: (2024)
by: Mostajeran, Farinaz, et al.
Published: (2024)
CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark
by: Wang, Hongyi, et al.
Published: (2024)
by: Wang, Hongyi, et al.
Published: (2024)
Space Group Informed Transformer for Crystalline Materials Generation
by: Cao, Zhendong, et al.
Published: (2024)
by: Cao, Zhendong, et al.
Published: (2024)
A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence Formulation
by: Hu, Xuan, et al.
Published: (2024)
by: Hu, Xuan, et al.
Published: (2024)
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
by: Shah, Karan, et al.
Published: (2024)
by: Shah, Karan, et al.
Published: (2024)
MESS: Modern Electronic Structure Simulations
by: Helal, Hatem, et al.
Published: (2024)
by: Helal, Hatem, et al.
Published: (2024)
Discovering High-Strength Alloys via Physics-Transfer Learning
by: Zhao, Yingjie, et al.
Published: (2024)
by: Zhao, Yingjie, et al.
Published: (2024)
Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning
by: Zhang, Ruiqi, et al.
Published: (2024)
by: Zhang, Ruiqi, et al.
Published: (2024)
A predictive machine learning force field framework for liquid electrolyte development
by: Gong, Sheng, et al.
Published: (2024)
by: Gong, Sheng, et al.
Published: (2024)
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
by: Avilov, Fedor S., et al.
Published: (2024)
by: Avilov, Fedor S., et al.
Published: (2024)
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
by: Taniai, Tatsunori, et al.
Published: (2024)
by: Taniai, Tatsunori, et al.
Published: (2024)
Design of 2D Skyrmionic Metamaterial Through Controlled Assembly
by: Xu, Qichen, et al.
Published: (2024)
by: Xu, Qichen, et al.
Published: (2024)
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
by: Vandermause, Jonathan, et al.
Published: (2024)
by: Vandermause, Jonathan, et al.
Published: (2024)
Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
by: Mehdizadeh, Ardavan, et al.
Published: (2025)
by: Mehdizadeh, Ardavan, et al.
Published: (2025)
Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles
by: Elsborg, Jonas, et al.
Published: (2025)
by: Elsborg, Jonas, et al.
Published: (2025)
Stress and heat flux via automatic differentiation
by: Langer, Marcel F., et al.
Published: (2023)
by: Langer, Marcel F., et al.
Published: (2023)
Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?
by: Li, Denan, et al.
Published: (2025)
by: Li, Denan, et al.
Published: (2025)
A comparative study of transformer models and recurrent neural networks for path-dependent composite materials
by: Uvdal, Petter, et al.
Published: (2026)
by: Uvdal, Petter, et al.
Published: (2026)
ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature
by: Roy, Aritra, et al.
Published: (2025)
by: Roy, Aritra, et al.
Published: (2025)
Physics-informed neural operator for predictive parametric phase-field modelling
by: Chen, Nanxi, et al.
Published: (2026)
by: Chen, Nanxi, et al.
Published: (2026)
Similar Items
-
MADAS -- A Python framework for assessing similarity in materials-science data
by: Kuban, Martin, et al.
Published: (2024) -
Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
by: Speckhard, Daniel T., et al.
Published: (2025) -
Cluster Expansion Toward Nonlinear Modeling and Classification
by: Stroth, Adrian, et al.
Published: (2025) -
On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
by: Rensmeyer, Tim, et al.
Published: (2025) -
Information-Theoretic Grid Topology Reconstruction using Low-Precision Smart Meter Data
by: Speckhard, Daniel T.
Published: (2025)