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Entities25 of 207,987
- large language modeltechnology1,043 mentions
- robotic manipulationmarket824 mentions
- artificial intelligencetechnology739 mentions
- reinforcement learningtechnology491 mentions
- density-functional theorytechnology386 mentions
- Transformertechnology371 mentions
- agentic AItechnology353 mentions
- vision-language-action modeltechnology350 mentions
- U.S. Food and Drug Administrationorganization320 mentions
- Vision-Language Modeltechnology318 mentions
- Microsoftorganization291 mentions
- Googleorganization276 mentions
- NVIDIAorganization264 mentions
- Model Context Protocoltechnology232 mentions
- machine learningtechnology204 mentions
- MarketsandMarketsorganization199 mentions
- Claudeproduct192 mentions
- AI agenttechnology188 mentions
- ChatGPTproduct178 mentions
- Anthropicorganization177 mentions
- integrated sensing and communicationtechnology175 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology169 mentions
- IBMorganization163 mentions
Entity
machine learning interatomic potential
technology85 mentionslast seen Nov 13, 2026
Models for predicting interatomic forces in molecular systems, fine-tuned across different chemical concentrations.
Relationships 50+
- machine learning interatomic potentialdepends ondensity-functional theory99% confidence7 sources
- molecular dynamics simulationdepends onmachine learning interatomic potential99% confidence6 sources
- machine learning interatomic potentialdepends onactive learning99% confidence3 sources
- molecular dynamicsdepends onmachine learning interatomic potential99% confidence3 sources
- machine learning interatomic potentialapplies toatomistic simulation99% confidence2 sources
- machine learning interatomic potentialapplies tomolecular dynamics simulation99% confidence2 sources
- Crystal REPresentation Alignmentdepends onmachine learning interatomic potential95% confidence1 source
- LightPFPdepends onmachine learning interatomic potential95% confidence1 source
- SLUSCHI-UPdepends onmachine learning interatomic potential94% confidence1 source
- machine learning interatomic potentialdepends onmean squared error94% confidence1 source
- Contrastive Regularized MSEdepends onmachine learning interatomic potential93% confidence1 source
- molecular augmented dynamicsdepends onmachine learning interatomic potential93% confidence1 source
- chemtrain-deployimplementsmachine learning interatomic potential93% confidence1 source
- machine learning interatomic potentialapplies todiamond particle92% confidence1 source
- machine learning interatomic potentialapplies tomaterials science simulation92% confidence1 source
- machine learning interatomic potentialapplies tosilica92% confidence1 source
- thermodynamic correction layerdepends onmachine learning interatomic potential92% confidence1 source
- machine learning interatomic potentialapplies tosilicon nitride92% confidence1 source
- massively overlapping cascade simulationdepends onmachine learning interatomic potential91% confidence1 source
- machine learning interatomic potentialapplies tohydroxide transport90% confidence1 source
- machine learning interatomic potentialapplies todynamical stability in inorganic semiconductor nanocrystals90% confidence1 source
- ALIGNN-FFimplementsmachine learning interatomic potential90% confidence1 source
- MACEdepends onmachine learning interatomic potential90% confidence1 source
- machine learning interatomic potentialapplies toelectrochemistry simulation90% confidence1 source
- machine learning interatomic potentialdepends onmolecular dynamics90% confidence1 source
- machine learning interatomic potentialdepends onPANNA 2.090% confidence1 source
- machine learning interatomic potentialapplies tometal alloy90% confidence1 source
- machine learning interatomic potentialdepends onfirst-principles force prediction90% confidence1 source
- SevenNet-Nanodepends onmachine learning interatomic potential90% confidence1 source
- machine learning interatomic potentialapplies toelectrocatalysis90% confidence1 source
- machine learning interatomic potentialdepends onLATTE descriptor90% confidence1 source
- MoiréMLIPdepends onmachine learning interatomic potential90% confidence1 source
- machine learning interatomic potentialdepends onquantum-computing-based electronic-structure calculation90% confidence1 source
- machine learning interatomic potentialdepends onTATB90% confidence1 source
- machine learning interatomic potentialdepends onsilicon carbonitride90% confidence1 source
- melt-quench molecular dynamicsdepends onmachine learning interatomic potential90% confidence1 source
- PESCERT certificationapplies tomachine learning interatomic potential90% confidence1 source
- machine learning interatomic potentialapplies tomaterials discovery90% confidence1 source
- trajectory-level fine-tuningdepends onmachine learning interatomic potential90% confidence1 source
- attention-coupled neural networks potentialdepends onmachine learning interatomic potential90% confidence1 source
- machine learning interatomic potentialapplies todrug design90% confidence1 source
- machine learning interatomic potentialdepends onab initio calculation90% confidence1 source
- DPA4C-LRdepends onmachine learning interatomic potential88% confidence1 source
- PaiNNimplementsmachine learning interatomic potential88% confidence1 source
- machine learning interatomic potentialapplies tosimulation-driven materials design88% confidence1 source
- machine learning interatomic potentialapplies tocalcium oxide88% confidence1 source
- machine learning interatomic potentialdepends onstochastic self-consistent harmonic approximation88% confidence1 source
- machine learning interatomic potentialdepends onuncertainty quantification88% confidence1 source
- machine learning interatomic potentialdepends onautomatic differentiation88% confidence1 source
- MACEimplementsmachine learning interatomic potential88% 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