Postdoctoral Researcher · IISc Bengaluru

Manas
Sharma

PhD in Physics · Computational Materials Scientist

I develop quantum embedding methods, machine-learned interatomic potentials, and computational tools to understand light–matter interactions, thin-film growth, and the electronic structure of complex materials — at the atomic scale.

View Publications Download CV ↗
Manas Sharma
11 Papers
442+ Citations
8 h-index

Updates

News

Jan 2026

PyFock released

PyFock, a pure-Python MIT-licensed DFT code with molecular integrals and exchange-correlation terms, was released.

Jul 2025

Phys Whiz crossed 20k subscribers

Phys Whiz reached 20k subscribers with tutorials and lectures on physics, computational materials science, and numerical methods.

May 2025

MLIP Playground released

MLIP Playground launched for running, benchmarking, and testing machine-learned interatomic potentials.

May 2024

Cube Suite released

Cube Suite, a web app for processing and manipulating CUBE files, was released.

Mar 2024

PhD thesis defended

Defended the PhD thesis with the summa cum laude grade.

Jul 2023

RIPER-TOOLS released

RIPER-TOOLS launched for creating TURBOMOLE RIPER input files from CIF, XYZ, POSCAR, Materials Project, and PubChem sources.

Background

About Me

I am a postdoctoral researcher in the Chemical Engineering Department at the Indian Institute of Science (IISc), jointly advised by Prof. Ananth Govind Rajan and Prof. Sudeep Punnathanam. My current work focuses on developing graph neural network (GNN)-based interatomic potentials for simulating thin-film growth processes such as CVD and PVD.

Previously, I obtained my Ph.D. (Physics) from Friedrich Schiller University Jena, Germany, with the highest distinction of summa cum laude, under Prof. Dr. Marek Sierka. During my doctorate I developed and implemented advanced DFT-based quantum embedding methods coupled with wavefunction and real-time TDDFT approaches, contributing new capabilities to the TURBOMOLE quantum chemistry package.

Beyond research, I am deeply invested in scientific outreach: I run the Phys Whiz YouTube channel (~21k subscribers, 3.6M views), build open-source computational tools, and mentor students worldwide.

DFT RT-TDDFT Quantum Embedding GNN Potentials MLIPs CVD / PVD NEB TURBOMOLE PySCF VASP Python Fortran

Quick Info

Focus Areas

Research Interests

Embedding

Quantum Embedding Methods

Developing DFT-based embedding methods coupled with wavefunction theory (WFT) and RT-TDDFT to achieve high-accuracy excited-state and adsorption energy calculations at reduced cost.

MLIPs

Machine-Learned Potentials

Training graph neural network (GNN)-based interatomic potentials on high-fidelity DFT data to enable large-scale atomistic simulations of thin-film deposition (CVD/PVD).

RT-TDDFT

Light–Matter Interactions

Studying high harmonic generation and optical excitations in molecular thin films using real-time TDDFT and GW/BSE approaches within periodic and molecular frameworks.

Growth

Thin-Film Growth

Investigating reaction pathways and energy barriers for CVD/PVD using DFT, nudged elastic band (NEB), and ab initio molecular dynamics (AIMD) simulations.

Code

Computational Tool Development

Building performant DFT codes, neural network libraries, and GUI applications — optimized for parallelization and GPU acceleration — to serve the research community.

Outreach

Scientific Outreach

Creating YouTube tutorials, web and Android apps, and blog content to make computational materials science accessible to students and researchers worldwide.

Trajectory

Education & Experience

2024 - Present

Postdoctoral Researcher · IISc Bangalore

Developing GNN-based MLIPs for thin-film deposition and materials design, with DFT, NEB, AIMD, and molecular dynamics workflows.

2019 - 2024

PhD Physics · Friedrich Schiller University Jena

Summa cum laude. Thesis on density functional theory based embedding for molecular and periodic systems.

2019 - 2024

Scientific Employee · FSU Jena

Implemented quantum embedding and real-time TDDFT capabilities in the TURBOMOLE RIPER module using Fortran and Python.

2016 - 2018

MSc Physics · University of Delhi

Specialized in nanoscience, ranked third, and completed DFT-based research that led to publications.

2013 - 2016

BSc (Hons.) Physics · University of Delhi

Acharya Narendra Dev College. Ranked second with 87.08 percent and received the DC Arora scholarship.

2014 - Present

BragitOff & Phys Whiz

Writing tutorials, building apps, and creating physics outreach content through BragitOff.com and Phys Whiz.

Recent Work

Selected Publications

All Publications →   Google Scholar ↗

Code Development

Tools & Applications

Python DFT

PyFock

Pure-Python DFT code with molecular integrals and exchange-correlation terms.

Project site

MLIP Web App

MLIP Playground

No-code web app for running, testing, and comparing machine-learned interatomic potentials.

Launch app

Quantum Chemistry

RIPER-Tools

Input generation tools for the RIPER module of TURBOMOLE from structure files and databases.

Open tool

Visualization

CrysX 3D Viewer

Molecule and crystal visualizer for publication-quality structures and animations.

Project page

CUBE Files

Cube Suite

Web app for processing and manipulating CUBE files from quantum chemistry programs.

Open app

More Tools

CrysX, converters, demos

Android apps, file converters, basis-set tools, DFT embedding demos, and ML demos.

All code projects

Get in Touch

Let's Connect

Whether it's research collaboration, outreach, or just a conversation about computational physics — I'd love to hear from you.

[email protected]
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