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Entities25 of 207,987
- large language modeltechnology1,048 mentions
- robotic manipulationmarket834 mentions
- artificial intelligencetechnology739 mentions
- reinforcement learningtechnology492 mentions
- density-functional theorytechnology386 mentions
- Transformertechnology372 mentions
- vision-language-action modeltechnology355 mentions
- agentic AItechnology353 mentions
- Vision-Language Modeltechnology321 mentions
- U.S. Food and Drug Administrationorganization320 mentions
- Microsoftorganization291 mentions
- Googleorganization277 mentions
- NVIDIAorganization265 mentions
- Model Context Protocoltechnology232 mentions
- machine learningtechnology204 mentions
- MarketsandMarketsorganization199 mentions
- Claudeproduct192 mentions
- AI agenttechnology188 mentions
- ChatGPTproduct178 mentions
- Anthropicorganization177 mentions
- integrated sensing and communicationtechnology176 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology170 mentions
- IBMorganization163 mentions
Entity
active learning
technology38 mentionslast seen Nov 13, 2026
An inverse-design strategy examined for integrating with conditional generation in materials design.
Relationships 36
- machine learning interatomic potentialdepends onactive learning99% confidence3 sources
- Calpricdepends onactive learning99% confidence2 sources
- active learningdepends onflow matching92% confidence1 source
- LEAPdepends onactive learning92% confidence1 source
- spin-MLIPimplementsactive learning92% confidence1 source
- fractional-order viscoelastic modeldepends onactive learning91% confidence1 source
- closed-loop molecule generation pipelinedepends onactive learning90% confidence1 source
- active logistic regressiondepends onactive learning90% confidence1 source
- active learningdepends ondeep ensemble90% confidence1 source
- ActiveKDdepends onactive learning90% confidence1 source
- active learningdepends onhuman-in-the-loop optimization89% confidence1 source
- High-Utility Sequential Pattern Mining using SHAP valuesdepends onactive learning88% confidence1 source
- gated active learning frameworkdepends onactive learning88% confidence1 source
- Bayesian model-based reinforcement learningdepends onactive learning88% confidence1 source
- uncertainty quantificationapplies toactive learning87% confidence1 source
- last-layer-projection regressiondepends onactive learning85% confidence1 source
- OLASdepends onactive learning85% confidence1 source
- active learningapplies tohydrogen and ion diffusion simulation85% confidence1 source
- active learningapplies toreactive and photochemical dynamics simulation85% confidence1 source
- active learningapplies toferroelectric materials simulation85% confidence1 source
- active learningapplies toelemental and alloy mechanics85% confidence1 source
- active learningdepends onuncertainty quantification85% confidence1 source
- active learningdepends onCALPHAD85% confidence1 source
- active learningdepends onmachine learning interatomic potential85% confidence1 source
- active learningdepends onGaussian Process Regression85% confidence1 source
- Thea active learning platformdepends onactive learning85% confidence1 source
- Ultra Strong Machine Learningdepends onactive learning85% confidence1 source
- bifidelity surrogate modeldepends onactive learning85% confidence1 source
- ActMOFdepends onactive learning85% confidence1 source
- on-the-fly molecular dynamicsdepends onactive learning85% confidence1 source
- surrogate modelingdepends onactive learning85% confidence1 source
- Random Forestimplementsactive learning85% confidence1 source
- active learningapplies tomechanical metamaterials design80% confidence1 source
- active learningdepends onvariance-based acquisition criteria80% confidence1 source
- kernel-based acquisitiondepends onactive learning80% confidence1 source
- deep equivariant neural networkimplementsactive learning75% confidence1 source
Relationship
machine learning interatomic potentialdepends onactive learning
99% confidence3 supporting mentions
Either name opens that entity’s relationships.
Sources 3
- preprintarXivOct 28, 2026Learning Between the Lines: Interaction Interpolation in a Multi Element Machine Learning Interatomic Potential
we utilize a step-wise active learning (AL) approach to develop a compact yet diverse dataset and model
- preprintarXivOct 16, 2026Active Learning for Interatomic Potentials: A Review of Uncertainty-Driven Data Efficiency
Developing a machine learning interatomic potential (MLIP) that is accurate for the specific conditions under which it will be applied. Active learning (AL) addresses this challenge by closing the loop between candidate generation, data selection, reference calculation, and model updating.
- preprintarXivJul 3, 2026Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling
Accurate lightweight machine-learning potentials -- developed via closed-loop active learning