Knowledge Graph
Graph explorer
Search resolved entities, walk their edges, and read the sentence behind every one.
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
- 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 communicationtechnology176 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology170 mentions
- IBMorganization163 mentions
Entity
molecular dynamics simulation
technology63 mentionslast seen Nov 13, 2026
Computational method used to model ionic conduction and hydration effects in layered materials.
Relationships 45
- molecular dynamics simulationdepends onmachine learning interatomic potential99% confidence6 sources
- molecular dynamics simulationapplies totungsten99% confidence2 sources
- molecular dynamics simulationdepends onmachine-learned interatomic potential99% confidence2 sources
- molecular dynamics simulationdepends onmachine-learned interaction potential93% confidence1 source
- molecular dynamics simulationapplies toCu-Zr metallic glass92% confidence1 source
- molecular dynamics simulationdepends onequivariant neural network force field92% confidence1 source
- graphyne kirigamidepends onmolecular dynamics simulation92% confidence1 source
- ProtScapedepends onmolecular dynamics simulation92% confidence1 source
- molecular dynamics simulationdepends onmachine-learned force field90% confidence1 source
- colloidal self-assemblydepends onmolecular dynamics simulation90% confidence1 source
- molecular dynamics simulationdepends onmachine learning force field90% confidence1 source
- molecular dynamics simulationapplies tobarium zirconate titanate90% confidence1 source
- interfacial phonon biasdepends onmolecular dynamics simulation90% confidence1 source
- molecular dynamics simulationapplies topolymer-derived ceramic90% confidence1 source
- molecular dynamics simulationdepends onatomic cluster expansion90% confidence1 source
- molecular dynamics simulationapplies toAlTi metallic alloy90% confidence1 source
- multiscale modelingdepends onmolecular dynamics simulation88% confidence1 source
- molecular dynamics simulationapplies tosilicon nanoparticle88% confidence1 source
- lattice-responsive frameworkcomposed ofmolecular dynamics simulation88% confidence1 source
- molecular dynamics simulationapplies tonickel88% confidence1 source
- covalent organic frameworkdepends onmolecular dynamics simulation88% confidence1 source
- TIP4P/2005implementsmolecular dynamics simulation85% confidence1 source
- molecular dynamics simulationapplies towurtzite ferroelectric85% confidence1 source
- molecular dynamics simulationapplies tomaterials design and characterization85% confidence1 source
- molecular dynamics simulationapplies tocovalent organic framework characterization85% confidence1 source
- molecular dynamics simulationdepends onmachine learning85% confidence1 source
- molecular dynamics simulationdepends ontwo-dimensional crystal85% confidence1 source
- molecular dynamics simulationdepends ondensity-functional theory85% confidence1 source
- molecular dynamics simulationdepends onedge dislocation85% confidence1 source
- molecular dynamics simulationdepends onanisotropic nanoparticle rejamming85% confidence1 source
- molecular dynamics simulationdepends onionic liquid85% confidence1 source
- molecular dynamics simulationdepends onvacancy loop85% confidence1 source
- oilseed rape straw biochardepends onmolecular dynamics simulation85% confidence1 source
- tabulated Gaussian approximation potentialdepends onmolecular dynamics simulation85% confidence1 source
- MolCryst-MLIPsdepends onmolecular dynamics simulation85% confidence1 source
- molecular dynamics simulationapplies tobiomolecular dynamics modeling85% confidence1 source
- molecular dynamics simulationapplies tophase stability of ionic oxides80% confidence1 source
- molecular dynamics simulationapplies tosupercooled water physics80% confidence1 source
- molecular dynamics simulationapplies toirradiation damage mitigation80% confidence1 source
- polypseudorotaxanedepends onmolecular dynamics simulation80% confidence1 source
- molecular dynamics simulationapplies tohazardous-waste encapsulation80% confidence1 source
- molecular dynamics simulationimplementsionic transport80% confidence1 source
- molecular dynamics simulationdepends ontensile twinning80% confidence1 source
- molecular dynamics simulationapplies toionic transport80% confidence1 source
- molecular dynamics simulationapplies tohumid environment material applications75% confidence1 source
Relationship
molecular dynamics simulationapplies totungsten
99% confidence2 supporting mentions
Either name opens that entity’s relationships.
Sources 2
- preprintarXivNov 8, 2026Hydrogen Thermodiffusion in Tungsten: Trapped Hydrogen Distorts Heat of Transport Estimation from Total Concentration Profiles
Using molecular dynamics simulations, we find that the heat of transport, which quantifies the coupling between temperature gradients and hydrogen transport, for hydrogen in tungsten
- preprintarXivOct 28, 2026Strain-Induced BCC-to-FCC Transformation in Tungsten Uncovered by Molecular Dynamics Simulations with Machine Learning Potentials
we investigate the body centered cubic (BCC) to face centered cubic (FCC) transformation in tungsten via a rigorously validated machine learning potential (MLP) molecular dynamics (MD) approach