Physics

First-principles and computational condensed-matter physics: defects, two-dimensional materials, magnetism and topology.

DFT+U Machine learning Bayesian optimization Hybrid functionals GW Alloys DFT Infrared materials InSb Semiconductors Gaussian processes InAsSb alloys Band alignment Heterointerfaces Virtual crystal approximation 2D materials BSE DFPT Graph neural networks hBN ML interatomic potentials Molecular dynamics Persistent photoconductivity Point defects Pseudopotentials Quantum ESPRESSO

Now
Project
Persistent photoconductivity in hBN
Defect discovery and photoinduced doping in hexagonal boron nitride, aimed at deep-UV optoelectronics.
Physics AI/ML hBN 2D materials Point defects Persistent photoconductivity DFT GW BSE ML interatomic potentials Molecular dynamics
Mar 2026
Talk
ML-Guided Ab initio Modeling
Seminar, LPENS, École Normale Supérieure, Paris
AI/ML Physics Machine learning DFT
2025
Publication
Machine Learning-Driven First-Principles Modeling of Far-Infrared Materials
Ritwik Das. PhD thesis, Université Paris-Saclay, C2N (CNRS). NNT 2025UPAST100
Physics AI/ML Bayesian optimization Gaussian processes Machine learning DFT+U Hybrid functionals GW InSb InAsSb alloys Heterointerfaces Band alignment Virtual crystal approximation Alloys Infrared materials Semiconductors
PhD, completed 2025
Project
Machine-learning-driven modeling of far-infrared semiconductors
InSb, InAs, InAsSb alloys and CdTe/InSb heterointerfaces from first principles.
Physics Electronics Semiconductors Infrared materials InSb InAsSb alloys Heterointerfaces Alloys DFT DFT+U Hybrid functionals GW DFPT Band alignment Bayesian optimization Machine learning
2025
Project
Deep-learning prediction of ab initio parameters
A pipeline, from an MLP to a graph neural network, that infers Hubbard U and related parameters from atomic and electronic descriptors.
AI/ML Physics Graph neural networks Machine learning DFT+U Hybrid functionals
2025
Publication
Structural, 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
Physics Electronics InAsSb alloys Alloys Semiconductors Infrared materials Hybrid functionals GW DFT+U
2025
Publication
High-fidelity electronic structure and properties of InSb: G0W0 and Bayesian-optimized hybrid functionals and DFT + U approaches
Ritwik Das, Anne-Sophie Grimault-Jacquin, Frédéric Aniel. Physical Review B 112, 075136
Physics AI/ML InSb Semiconductors Infrared materials GW Hybrid functionals DFT+U Bayesian optimization Machine learning
2024
Publication
BMach: a Bayesian machine for optimizing Hubbard U parameters in DFT+U with machine learning
R. Das. arXiv preprint
AI/ML Physics Bayesian optimization Gaussian processes Machine learning DFT+U
Feb 2023
Writing
Virtual crystal approximation (VCA)
Mixing pseudopotentials to run DFT calculations for alloys such as InSb(x)As(1-x) with Quantum ESPRESSO.
Physics Virtual crystal approximation Alloys DFT Quantum ESPRESSO Pseudopotentials InSb
2023
Talk
Bayesian Machine: Optimizing the Hubbard U Parameter in DFT+U With Machine Learning
Conference contribution, European Materials Research Society (E-MRS) Conference
AI/ML Physics Bayesian optimization Machine learning DFT+U
2023–2024
Project
BMach, Bayesian optimization of Hubbard U
A Bayesian framework that calibrates Hubbard U and hybrid-functional parameters at a small fraction of the cost of direct high-level methods.
AI/ML Physics Bayesian optimization Gaussian processes Machine learning DFT+U Hybrid functionals