AI-Driven Nanomaterial Discovery & Design
Discover faster. Design smarter. Build next-gen nanomaterials with AI.
Skills you will gain:
About Program:
Nanomaterial discovery traditionally relies on iterative experiments and expensive simulations. AI is transforming this process by enabling rapid screening of compositions, predicting structure–property relationships, and guiding experiments toward the most promising candidates. From nanoparticles and 2D materials to nanocomposites and quantum dots, machine learning supports faster innovation with reduced cost and improved design accuracy.
This workshop introduces end-to-end AI workflows for nanomaterials—from data collection and curation to feature engineering, model development, and optimization. Participants will explore how ML is used with computational materials tools (DFT/MD outputs, materials databases) and how methods like active learning and Bayesian optimization can suggest the next best experiments. Dry-lab sessions will focus on real datasets, practical modeling steps, and case studies relevant to modern materials R&D.
Aim: This workshop aims to train participants to accelerate nanomaterial discovery and design using AI/ML-driven workflows. It covers how data, simulations, and experiments can be combined to predict material properties and optimize composition, structure, and synthesis conditions. Participants will learn practical strategies for building datasets, selecting descriptors, training models, and interpreting predictions for real nanomaterials problems. The program is designed to support research and industry R&D in energy, electronics, catalysis, and biomedical materials.
Program Objectives:
- Understand AI’s role in nanomaterial discovery and design pipelines.
- Learn dataset creation, cleaning, and materials descriptor engineering.
- Train ML models to predict key properties (bandgap, stability, toxicity, conductivity, etc.).
- Apply optimization methods to propose new material candidates or synthesis conditions.
- Evaluate models and communicate results as research-grade design decisions.
What you will learn?
Day 1 – Foundations of AI in Nanomaterial Science
Introduction to Nanomaterial Property–Structure Relationships
Overview of AI/ML Workflows in Materials Science
Data Sources for Nanomaterials (Materials Project, AFLOW, NOMAD)
Day 2 – Machine Learning Models for Nanomaterial Prediction
Feature Engineering & Descriptor Generation for Nanomaterials
Supervised Learning Models for Bandgap, Stability & Toxicity Prediction
Hands-on: Building ML Models for Nanomaterial Property Prediction
Day 3 – Advanced Models & Autonomous Discovery
Graph Neural Networks (GNNs) for Crystal & Nanostructures
High-Throughput Screening & Active Learning
Hands-on: AI-Driven Optimization of Nanomaterial Design
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes
Participants will be able to:
- Build a clean dataset for a nanomaterials problem and select meaningful features.
- Train and evaluate ML models for materials property prediction.
- Use optimization logic to shortlist promising nanomaterial candidates.
- Interpret model outputs to support experimental decision-making.
- Create a mini “materials discovery report” suitable for R&D use.