AI-Driven Materials Discovery: Machine Learning Potentials, Molecular Dynamics & High-Throughput Screening
Accelerate Materials Innovation with AI: From Data-Driven Discovery to Molecular Simulation and Automated Screening
Skills you will gain:
About Program:
Artificial Intelligence is revolutionizing materials science by enabling researchers to discover, design, and optimize advanced materials faster and more efficiently. This workshop provides a comprehensive introduction to AI-driven materials discovery, integrating machine learning, materials informatics, machine learning potentials, molecular dynamics simulations, and high-throughput screening approaches.
Participants will explore how AI models utilize large-scale materials datasets to predict properties, simulate atomic-level behaviour, and identify promising material candidates for applications in energy storage, catalysis, nanotechnology, semiconductors, and advanced functional materials.
Through a combination of conceptual learning and hands-on computational sessions using open-source tools, participants will gain practical exposure to modern AI workflows used in computational materials research.
Aim: The aim of this workshop is to provide researchers, academicians, and industry professionals with a practical understanding of how Artificial Intelligence and computational approaches are transforming materials discovery, enabling faster prediction, simulation, and optimization of next-generation materials.
Program Objectives:
Participants will learn to:
- Understand the fundamentals of AI-driven materials discovery and materials informatics workflows.
- Explore open-source materials databases and prepare datasets for AI-based analysis.
- Apply machine learning approaches for predicting material properties and performance.
- Understand machine learning interatomic potentials for accelerated atomic simulations.
- Learn molecular dynamics simulation concepts for studying material behaviour at atomic scales.
- Develop high-throughput screening workflows for automated material identification and ranking.
- Explore emerging AI approaches including generative AI, active learning, and autonomous materials discovery.
What you will learn?
📅 Day 1: AI-Driven Materials Discovery & Machine Learning for Materials Informatics
- Focus: Understanding AI-based materials discovery workflows, materials data, and machine learning approaches for predicting material properties.
- Introduction to AI in materials science and its applications in energy materials, nanomaterials, catalysts, semiconductors, and advanced materials.
- Overview of materials informatics and open-source research databases:
- Materials Project
- OQMD
- AFLOW
- NOMAD
- Understanding materials representation:
- Crystal structures
- Chemical descriptors
- Atomic features
- Property-based datasets
- Introduction to machine learning models for materials prediction:
- Regression models
- Random Forest
- Gradient Boosting
- Neural Networks
- Graph Neural Networks
- Developing AI workflows for material property prediction:
Data Preparation → Feature Extraction → Model Training → Property Prediction - Understanding model evaluation, validation strategies, and prediction accuracy metrics.
🛠️ Hands-on:
- Build an AI-based material property prediction model using open-source materials datasets.
🧰 Tools Covered: Google Colab, Python, Pymatgen, Matminer, Materials Project API, Scikit-learn, Pandas, Matplotlib
📅 Day 2: Machine Learning Potentials & Molecular Dynamics Simulation
- Focus: Learning AI-based atomic simulation methods and machine learning potentials for accelerated materials modelling.
- Introduction to molecular dynamics and atomic-level simulation approaches in materials research.
- Understanding Machine Learning Interatomic Potentials (MLIPs) and their role in replacing traditional force fields.
- Exploring advanced ML potential frameworks:
- DeepMD
- MACE
- NequIP
- Gaussian Approximation Potential (GAP)
- Understanding AI-based prediction of:
- Atomic energies
- Interatomic forces
- Structural behaviour
- Molecular dynamics workflow:
Structure Preparation → Energy Optimization → Simulation → Trajectory Analysis - Applications of AI-assisted molecular dynamics:
- Material stability
- Diffusion behaviour
- Thermal properties
- Defect analysis
🛠️ Hands-on:
- Perform an AI-assisted molecular dynamics simulation using machine learning potentials.
🧰 Tools Covered: Google Colab, Python, ASE, MACE, ML Potentials, OVITO, Matplotlib
📅 Day 3: High-Throughput Screening & AI-Based Materials Discovery Pipeline
- Focus: Building automated AI-driven workflows for screening, ranking, and identifying promising materials candidates.
- Introduction to high-throughput materials screening and automated computational discovery workflows.
- Understanding AI-based material filtering and ranking strategies using large-scale materials databases.
- Property-based screening approaches:
- Stability prediction
- Formation energy analysis
- Band gap prediction
- Mechanical and thermal property evaluation
- Developing computational pipelines:
Database → AI Prediction → Screening → Candidate Ranking - Exploring AI optimization approaches:
- Active learning
- Generative AI for materials design
- Multi-objective optimization
- Applications of AI materials discovery:
- Battery materials
- Catalysts
- Hydrogen storage
- Semiconductor materials
- Nanomaterials
🛠️ Hands-on:
- Develop an AI-based high-throughput screening workflow to identify and rank potential material candidates.
🧰 Tools Covered: Google Colab, Python, Materials Project API, Pymatgen, Matminer, Scikit-learn, Plotly, Matplotlib
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
This workshop is designed for:
Researchers & PhD Scholars
- Materials Science
- Nanotechnology
- Computational Chemistry
- Physics
- Chemistry
- Chemical Engineering
- Energy Materials
- Computational Materials Modelling
Academicians & Faculty Members
- Researchers interested in integrating AI methodologies into materials research.
- Faculty members exploring computational approaches for teaching and research.
Industry Professionals
- R&D scientists working in:
- Advanced materials development
- Battery technologies
- Semiconductor research
- Catalysis
- Nanomaterials
- Materials informatics
Suitable For Participants Interested In:
- Artificial Intelligence in Materials Science
- Machine Learning for Scientific Research
- Molecular Simulation
- Computational Materials Discovery
- Data-Driven Material Design
Career Supporting Skills
Program Outcomes
After completing this workshop, participants will be able to:
- Understand the complete AI-powered materials discovery pipeline from data to prediction and validation.
- Work with open-access materials databases such as Materials Project, OQMD, and AFLOW.
- Build machine learning models for material property prediction.
- Understand and apply AI-based molecular simulation workflows.
- Gain practical knowledge of machine learning potentials and molecular dynamics approaches.
- Develop computational screening strategies for identifying promising materials.
- Apply AI-driven methodologies in research areas including energy materials, nanotechnology, catalysis, and advanced materials design.