Knowledge Graph
Graph explorer
Search resolved entities, walk their edges, and read the sentence behind every one.
Entities25 of 207,987
- large language modeltechnology1,042 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
- Claudeproduct191 mentions
- AI agenttechnology188 mentions
- Anthropicorganization177 mentions
- ChatGPTproduct177 mentions
- integrated sensing and communicationtechnology175 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology169 mentions
- IBMorganization163 mentions
Entity
density-functional theory
technology386 mentionslast seen Nov 13, 2026
A computational method used for high-throughput screening of altermagnetic materials.
Relationships 50+
- machine learning interatomic potentialdepends ondensity-functional theory99% confidence7 sources
- machine learningdepends ondensity-functional theory99% confidence4 sources
- tight-binding modeldepends ondensity-functional theory99% confidence3 sources
- density-functional theoryapplies toNi-Mn-Ga martensite99% confidence2 sources
- machine learning force fielddepends ondensity-functional theory99% confidence2 sources
- machine-learning molecular dynamicsdepends ondensity-functional theory99% confidence2 sources
- moment tensor potentialdepends ondensity-functional theory99% confidence2 sources
- machine-learned interatomic potentialdepends ondensity-functional theory99% confidence2 sources
- density-functional theorydepends onHubbard correction99% confidence2 sources
- density-functional theoryapplies tocomputational chemistry99% confidence2 sources
- twisted bilayer MoSSedepends ondensity-functional theory99% confidence2 sources
- density-functional theorydepends onmeta-generalized gradient approximation99% confidence2 sources
- density-functional theorydepends onlocal density approximation98% confidence2 sources
- surface passivationdepends ondensity-functional theory97% confidence2 sources
- density-functional theorydepends onHeyd-Scuseria-Ernzerhof hybrid functional97% confidence2 sources
- high-dimensional neural network potentialdepends ondensity-functional theory96% confidence1 source
- TB2Jdepends ondensity-functional theory96% confidence1 source
- effective cluster interactionsdepends ondensity-functional theory95% confidence1 source
- Equation Learner Neural Networkdepends ondensity-functional theory95% confidence1 source
- random-phase approximationdepends ondensity-functional theory95% confidence1 source
- density-functional theorydepends oncarbon monolayer95% confidence1 source
- Abinitimplementsdensity-functional theory95% confidence1 source
- physics-guided machine-learning frameworkdepends ondensity-functional theory95% confidence1 source
- density-functional theoryapplies tomanganese sulfide95% confidence1 source
- BaSnO3depends ondensity-functional theory95% confidence1 source
- Deep Potential methoddepends ondensity-functional theory95% confidence1 source
- germaniumdepends ondensity-functional theory95% confidence1 source
- UnIPHdepends ondensity-functional theory95% confidence1 source
- LatticeMinddepends ondensity-functional theory95% confidence1 source
- density-functional theoryapplies tosilicon95% confidence1 source
- deep neural network interatomic potentialdepends ondensity-functional theory95% confidence1 source
- density-functional perturbation theorydepends ondensity-functional theory95% confidence1 source
- multiscale simulation frameworkdepends ondensity-functional theory94% confidence1 source
- PAOFLOWdepends ondensity-functional theory94% confidence1 source
- phase-field fracture simulationdepends ondensity-functional theory93% confidence1 source
- electronic density representationdepends ondensity-functional theory93% confidence1 source
- numerical atomic orbital basisdepends ondensity-functional theory93% confidence1 source
- DFT + U methoddepends ondensity-functional theory93% confidence1 source
- machine learning accelerated atomistic modelingdepends ondensity-functional theory93% confidence1 source
- graph neural network interatomic potentialdepends ondensity-functional theory93% confidence1 source
- multi-orbital Anderson-Newns modeldepends ondensity-functional theory93% confidence1 source
- ab initio calculationdepends ondensity-functional theory93% confidence1 source
- ensemble Monte Carlodepends ondensity-functional theory93% confidence1 source
- Thomas-Fermi-von Weizsäcker approximationdepends ondensity-functional theory92% confidence1 source
- Co1/4TaSe2depends ondensity-functional theory92% confidence1 source
- tight-binding methoddepends ondensity-functional theory92% confidence1 source
- density-functional theorydepends onextended Hubbard correction92% confidence1 source
- AutoDrudedepends ondensity-functional theory92% confidence1 source
- many-body perturbation theorydepends ondensity-functional theory92% confidence1 source
- Nitrogen based electride superconductor Nb5Ir3N under pressure: multifunctional physical properties from DFT based first-principles investigationdepends ondensity-functional theory92% confidence1 source
Relationship
machine learning interatomic potentialdepends ondensity-functional theory
99% confidence7 supporting mentions
Either name opens that entity’s relationships.
Sources 7
- preprintarXivOct 27, 2026Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost
A machine learning potential predicts hydrogen evolution activity and stability at density-functional-theory accuracy.
- preprintarXivOct 23, 2026Contrastive Regularization of Machine Learning Potentials
MLIPs trained by minimizing the mean squared error (MSE) on density functional theory (DFT) energies and forces
- preprintarXivSep 22, 2026Towards Extended Active Learning for Modelling Ferroelectric Switching: the Need for 'Gold Standards'
training of machine-learning interatomic potentials (MLIP) for large-scale simulations of materials
- preprintarXivSep 18, 2026From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentials
refine a pretrained DFT-based uMLIP
- preprintarXivJul 20, 2026Precipitate phase selection and grain boundary morphology in Cu-Ni-Si-Mn alloys: A machine-learning interatomic potential study
The MLIP calculations reliably reproduce DFT-level energetics for interfacial bonding
- preprintarXivApr 10, 2026Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory
- preprintarXivNov 17, 2025Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential
DFT-accuracy machine learning potential