Faculty

Institution

Unit

Venue

Dallas Trinkle

Ivan Racheff Professor and Associate Head of Undergraduate Studies

UIUC

Affiliations

The Grainger College of Engineering

Department of Materials Science and Engineering

Materials Science & Engineering

Dallas Trinkle is currently serving as a Materials Science And Engineering Ivan Racheff Professor and Associate Head of Undergraduate Studies at Uiuc.

Honors

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Experiences

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Education

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Dallas Trinkle

UIUC

Ivan Racheff Professor and Associate Head of Undergraduate Studies

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materials science applied physics physics computational materials science metals condensed matter physics condensed matter density functional theory intermetallics nonferrous metal mechanics of materials microstructure dislocation interaction alloy phase diffusion deformation material research solid mechanics mechanical engineering material modeling material simulation magnesium alloys quantum chemistry defect formation crystal research crystal growth diffusion resistance diffusion process solid-state physics transformation kinetics alloy design numerical simulations microstructure-strength relationship solid-state chemistry ab-initio methods interface science refractory metals fracture mechanics electronic materials work hardening chemical physics computational thinking scientific computing defect tolerance severe plastic deformation electronic structure oxidation resistance phase equilibrium point defects metallurgical interaction surface analysis anomalous diffusion strain localization hydrogen transition math xmlns martensitic transformation continuum modeling chemical thermodynamics machine learning models lattice green function solute drag elastic stiffness coefficients edge dislocation bcc fe computational modules energy minimization numerical computation numerical method for partial differential equation magnesium-based composite thermal cross-slip stress onsager matrix flexible boundary conditions green function computational curriculum lattice green fcc ni defect properties mechanical behavior transport phenomena composite interface

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Computing Ternary Liquid Phase Diagrams: Fe-Cu-Ni. Physical Review Materials (2025)

Dallas R. Trinkle

intermetallicsmetalsnonferrous metalphase transitionscomputational materials sciencecondensed matterphase equilibriumalloy phaseapplied physicsinterface (matter)condensed matter physicssolid-state chemistry

Core Energies in Isolated Edge and Mixed Dislocations in Bcc Fe From First-Principles Energy Density Method. Physical Review Materials (2025)

Yang DanDallas R. Trinkle

dislocation interactiondefect formationphysicscondensed mattercomputational materials sciencehigh energy density physicssolid-state physicsbcc fedensity functional theorycore energiesisolated edgeapplied physicscondensed matter physicsmaterials science

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications. Chemistry of Materials (2025)

Lauren WayCatalin D. SpataruReese E. JonesDallas R. TrinkleAndrew J. E. RowbergJoel B. VarleyRobert B. WexlerChristopher M. SmythTyra C. DouglasSean R. BishopElliot J. FullerAnthony H. McDanielStephan LanyMatthew D. Witman

materials engineeringdefect formationadvanced materialscomputational materials scienceengineeringmaterial researchdefect toleranceelectronic materialsmaterial modelingmaterial simulationlow-dimensional structuresapplied physicshigh-temperature energy applicationsmaterials discoverymaterials science

Contributions to Diffusion in Complex Materials Quantified With Machine Learning. Physical Review Letters (2024)

Soham ChattopadhyayDallas R. Trinkle

statistical mechanicsdiffusion-based modelingtransport phenomenanumerical simulationsstatistical physicsphysicsdiffusion processcomputational materials sciencecomputational physicsstatistical distributioncomplex materialsmachine learningmaterial modelingdiffusionmaterial simulationmultiscale modelingapplied physicsmaterials science

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature, Clean Energy Applications. (2024)

Lauren WayCatalin SpataruReese JonesDallas TrinkleAndrew RowbergJoel VarleyRobert WexlerChristopher SmythTyra DouglasSean BishopElliot FullerAnthony McDanielStephan LanyMatthew Witman

physic aware machine learningclean energy applicationsdeep learningquantum chemistrycomputational materials sciencematerial researchmachine learningmaterial modelingneural network (machine learning)graph neural networksmachine learning researchvacancy defect thermodynamicslow-dimensional structuresapplied physicsdensity functional theorymaterials science

Discovering Melting Temperature Prediction Models of Inorganic Solids by Combining Supervised and Unsupervised Learning. The Journal of Chemical Physics (2024)

Vahe GharakhanyanLuke J. WirthJose A. Garrido TorresEthan EisenbergTing WangDallas R. TrinkleSnigdhansu ChatterjeeAlexander Urban

inorganic solidspredictive analyticsphysic aware machine learningknowledge discoverymachine learning modelsmelting temperaturecomputational materials sciencepredictive learningsupervised learningmachine learningmachine learning researchcomputational intelligencedata sciencethermal stabilitypattern recognitionmaterials science

Impact of Computation in Undergraduate Curriculum: Alumni Perspective. (2024)

B. ArgunAndre SchleifeCecilia LealDallas TrinkleNicola PerryJessica KrogstadMatthew GoodmanAntonia StattMarie Charpagne

engineering educationcomputer scienceinformaticsstem educationcomputer-based educationtechnologycomputing educationcomputational literacycomputational thinkinghigher educationmathematics educationeducational informaticssoftware engineering educationalumni perspectiveundergraduate curriculumcomputational moduleseducational technology

Impact of Transitions Between Online and Offline Learning During Covid-19 on Computational Curricular Reform: Student Perspective. (2024)

Yang DanAndre SchleifeDallas TrinklePinshane HuangCecilia Leal

engineering educationeducationdigital learningundergraduate researchcomputational curricular reformtechnologylearning scienceseducational researchcomputing educationonline educationmaterials sciencehigher educationdistance educationonline computational learningonline learningeducational technology

KINOSONS: Quantifying the Contributions to Diffusion in Complex Materials. arXiv (Cornell University) (2024)

Soham ChattopadhyayDallas R. Trinkle

statistical mechanicsdiffusion-based modelingphysic aware machine learningtransport phenomenanumerical simulationsstatistical physicsphysicsdiffusion processcomputational materials sciencecomputational physicsstatistical distributioncomplex materialsmaterial simulationdiffusionmultiscale modelingapplied physicsindividual contributionsmaterials science

Explainable Machine Learning for Hydrogen Diffusion in Metals and Random Binary Alloys. Physical Review Materials (2023)

Grace M. LuMatthew WitmanSapan AgarwalVitalie StavilaDallas R. Trinkle

computer sciencepredictive analyticsdata-driven predictionmachine learning modelsmetalschemical propertiescomputational materials sciencemachine learning researchcomputational intelligencedata sciencehydrogen diffusionmachine learning

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Dallas Trinkle

Ivan Racheff Professor and Associate Head of Undergraduate Studies

UIUC

Affiliations

The Grainger College of Engineering

Department of Materials Science and Engineering

Materials Science & Engineering

Dallas Trinkle is currently serving as a Materials Science And Engineering Ivan Racheff Professor and Associate Head of Undergraduate Studies at Uiuc.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Dallas Trinkle

UIUC

Ivan Racheff Professor and Associate Head of Undergraduate Studies

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Computing Ternary Liquid Phase Diagrams: Fe-Cu-Ni. Physical Review Materials (2025)

Dallas R. Trinkle

Core Energies in Isolated Edge and Mixed Dislocations in Bcc Fe From First-Principles Energy Density Method. Physical Review Materials (2025)

Yang DanDallas R. Trinkle

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications. Chemistry of Materials (2025)

Contributions to Diffusion in Complex Materials Quantified With Machine Learning. Physical Review Letters (2024)

Soham ChattopadhyayDallas R. Trinkle

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature, Clean Energy Applications. (2024)

Lauren WayCatalin SpataruReese JonesDallas TrinkleAndrew RowbergJoel VarleyRobert WexlerChristopher SmythTyra DouglasSean BishopElliot FullerAnthony McDanielStephan LanyMatthew Witman

Discovering Melting Temperature Prediction Models of Inorganic Solids by Combining Supervised and Unsupervised Learning. The Journal of Chemical Physics (2024)

Vahe GharakhanyanLuke J. WirthJose A. Garrido TorresEthan EisenbergTing WangDallas R. TrinkleSnigdhansu ChatterjeeAlexander Urban

Impact of Computation in Undergraduate Curriculum: Alumni Perspective. (2024)

B. ArgunAndre SchleifeCecilia LealDallas TrinkleNicola PerryJessica KrogstadMatthew GoodmanAntonia StattMarie Charpagne

Impact of Transitions Between Online and Offline Learning During Covid-19 on Computational Curricular Reform: Student Perspective. (2024)

Yang DanAndre SchleifeDallas TrinklePinshane HuangCecilia Leal

KINOSONS: Quantifying the Contributions to Diffusion in Complex Materials. arXiv (Cornell University) (2024)

Soham ChattopadhyayDallas R. Trinkle

Explainable Machine Learning for Hydrogen Diffusion in Metals and Random Binary Alloys. Physical Review Materials (2023)

Grace M. LuMatthew WitmanSapan AgarwalVitalie StavilaDallas R. Trinkle

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