SADHU LAB (Scientific AI and Deep Hybrid Understanding LAB)

At SADHU LAB (Scientific AI and Deep Hybrid Understanding Lab), we are dedicated to advancing research at the crossroads of theoretical chemistry, topological data analysis, machine learning, and deep learning.

Our MISSION is to combine traditional scientific methods with modern data-driven approaches to solve complex problems in chemical, environmental, material, and nuclear sciences.

We focus on developing hybrid methodologies that integrate quantum mechanics, molecular dynamics, metadynamics, and graph-theoretic analysis with artificial intelligence and deep learning models. Through this interdisciplinary approach, we aim to achieve deeper scientific insights, accelerate discovery, and enable predictive modeling in domains traditionally driven by first-principles theories.

At SADHU LAB, we are passionate about innovation, collaboration, and pushing the boundaries of computational science. We welcome students, researchers, and collaborators who are excited to work at the interface of science and AI.

Ph.D Students (Those Bold Enough to Ask 'Why?')

AMIT SILSWAL

Topic:

Information-enriched Transformation of Low Resolution Spectra of Radiation Detector Using Machine and Deep Learning Methods

anirudh chandra

Topic:

Multi Scale Modeling based investigation on Radiation Induced DNA damage repair Mechanisms.

Team Members (“Part of something bigger.”)

ISHA garg

Bhabha Atomic Research Centre (BARC), India

Research Topic– Investigating catalytic reaction mechanisms (DFT) and computational protein design (Alphafold, MD, MtD).

Naman k bharti

Homi Bhabha National Institute, India

Research Topic – Gaining mechanistic insights into complex molecular systems through multiscale modeling (DFT, MD, MtD).

Bhagyashree mohalkar

Pursuing B.Tech. in Computer Science and Engineering (AI & Data Science) at Sanjivani College of Engineering, Kopargaon, Maharashtra

Research Intern – Developing the authentication/login interface for pyDOSEIA and exploring Graph Neural Network (GNN)-based AI/ML architectures.

YASH BHOSALE

Pursuing B.Tech. in Computer Science, Yadavrao Tasgaonkar Institute of Engineering and Technology (YTIET), Karjat, Maharashtra, India

Research Intern – Developing deep reinforcement learning algorithms for scientific and engineering applications.

Karthik Sundaresh Iyer

Pursuing B.Tech. in Computer Science and Engineering (AI & Data Science), SASTRA Deemed University, Thanjavur, Tamil Nadu, India

Past Research Intern – Developed Graph Neural Network (GNN)-based methodologies for applications at the interface of computational chemistry and artificial intelligence.

Past Team Members

Kshiraja chandramani

Pursuing B.Tech. in Computer Science and Engineering (AI & Data Science) Amity University Haryana, Gurugram, Haryana, India

Past Research Intern – Contributed to the documentation and benchmarking of pyDOSEIA.

kalpak gupte

Pursuing B.Tech. in Computer Science and Engineering (AI & Data Science) MIT World Peace University, Pune, Maharashtra, India

Past Research Intern – Developed the graphical user interface (GUI) for pyDOSEIA and contributed to the development of the PlumeDoseNet AI/ML architecture.

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