Dr. Kritesh Kumar Gupta

Assistant Professor (Research Track Faculty)

Amrita School of Artificial Intelligence,

Amrita Vishwa Vidyapeetham, Coimbatore, India

Google Scholar: https://scholar.google.com/citations?user=T8qa1l4AAAAJ&hl=en

Email: g_kriteshkumar@cb.amrita.edu,

Research

Publications

Teaching & Mentoring

Research Interests:

Data-driven Materials Discovery, Digital Twin, Artificial Intelligence, Multiscale Material Modelling & Analysis, 2D materials and heterostructures, Multi-scale mechanics, Nano-mechanics, Stochastic analysis

Teaching Interests:

Introduction to AI and ML, machine learning, DeEp Learning, Materials Informatics, Computational Materials science, Uncertainty quantification, Nanomaterials and nanotechnology, computational programming, discrete mathematics

Skillset:

Applied MACHINE Learning, DATA DRIVEN DESIGN AND OPTIMIZATION, Molecular Dynamics Simulation (LAMMPS, GROMACS), Density Functional Theory (Quantum Espresso), Finite Element Analysis (ANSYS), Solid Modelling (CATIA), Programming (MATLAB, Python)

I am currently serving as an Assistant Professor (Research Track Faculty) in the Department of Artificial Intelligence at Amrita Vishwa Vidyapeetham, Coimbatore. My academic and research trajectory lies at the intersection of artificial intelligence and computational modeling in materials and manufacturing, with expertise in atomistic materials design, materials informatics, and digital twin technology.

At Amrita (ranked among the top 10 universities in NIRF), I teach interdisciplinary courses across UG and PG levels, including:

• Fundamentals of AI & ML

• Machine Learning

• Materials Informatics

• Data-Driven Materials Modeling and Simulation

My research focuses on developing AI-driven frameworks—including neural networks, large language models (LLMs), Transformer architectures, and NLP—to enable combinatorial design of advanced materials like high-entropy alloys, ceramics, and perovskites for energy storage and structural applications. These models are integrated with atomistic simulations (Molecular Dynamics and Density Functional Theory) to create digital twins, reducing computational and experimental screening costs.

🔬 Research Highlights

• 30+ peer-reviewed publications in journals like Scientific Reports, Applied Energy, JMST

• 2 patents filed (1 published)

• 10+ international conference presentations

• 2 book chapters authored

• 1 authored book: “Machine Learning in Nanoscale Materials Design” (Springer, upcoming)

• Supervised 4 PG dissertations; currently guiding 1 Ph.D. student

I maintain active collaborations with top institutions including:

• University of Southampton

• University of Leeds (UK)

• Dusseldorf University (Germany)

Mission First

Panel Discussion on prospects of AI in Manufacturing