- 2025
PublicationMachine Learning-Driven First-Principles Modeling of Far-Infrared Materials
Ritwik Das. PhD thesis, Université Paris-Saclay, C2N (CNRS). NNT 2025UPAST100
PhysicsAI/MLBayesian optimizationGaussian processesMachine learningDFT+UHybrid functionalsGWInSbInAsSb alloysHeterointerfacesBand alignmentVirtual crystal approximationAlloysInfrared materialsSemiconductors
- PhD, completed 2025
ProjectMachine-learning-driven modeling of far-infrared semiconductors
InSb, InAs, InAsSb alloys and CdTe/InSb heterointerfaces from first principles.
PhysicsElectronicsSemiconductorsInfrared materialsInSbInAsSb alloysHeterointerfacesAlloysDFTDFT+UHybrid functionalsGWDFPTBand alignmentBayesian optimizationMachine learning
- 2025
PublicationStructural, Electronic Properties and Band Gap Bowing Parameters of CuPt-Ordered InAsxSb1−x Alloys: First-Principles Insights from HSE, G0W0, mBJ, and DFT+U
R. Das, A.-S. Grimault-Jacquin, F. Aniel. Submitted to Physical Review Materials
PhysicsElectronicsInAsSb alloysAlloysSemiconductorsInfrared materialsHybrid functionalsGWDFT+U
- Feb 2023
WritingVirtual crystal approximation (VCA)
Mixing pseudopotentials to run DFT calculations for alloys such as InSb(x)As(1-x) with Quantum ESPRESSO.
PhysicsVirtual crystal approximationAlloysDFTQuantum ESPRESSOPseudopotentialsInSb