AI-Driven Nanomaterials Discovery: Property Prediction, Screening & Explainable Materials Design

• Understand the role of AI and materials informatics in modern materials discovery.
• Explore databases containing computational and experimental materials information.
• Retrieve and organise material composition, structural and property data.
• Use Python-based materials-science libraries for data preparation.
• Generate meaningful composition- and structure-based materials descriptors.
• Perform exploratory analysis of structure–property relationships.
• Build machine-learning models for materials-property prediction.
• Compare regression and classification approaches for materials screening.
• Apply Random Forest and XGBoost to materials datasets.
• Evaluate models using appropriate performance metrics.
• Use explainable AI to identify important material descriptors.
• Perform computational screening of candidate materials.
• Rank promising materials based on predicted properties.
• Interpret AI predictions from a materials-science perspective.
• Develop a reproducible workflow for AI-guided materials discovery.

Day 1: Disease Variant Interpretation & Prime-Editing Strategy

• Introduce precision genome engineering and compare prime editing with conventional CRISPR-Cas editing and base-editing approaches.
• Identify disease-associated variants using ClinVar, interpret genomic coordinates, alleles, transcripts and variant annotations, and assess functional consequences using Ensembl VEP.
• Retrieve reference genomic and transcript sequences, examine the local sequence context, and determine suitable preliminary correction strategies including PE2, PE3, PE3b and twinPE approaches.

Day 2: AI-Guided pegRNA Design & Prime-Editing Outcome Prediction

• Understand pegRNA architecture, including spacer, primer-binding site (PBS) and reverse-transcription template (RTT), and examine how sequence context influences prime-editing efficiency.
• Generate computational pegRNA and nicking-guide designs for PE2, PE3 and PE3b strategies, and explore twinPE approaches for more complex sequence modifications where applicable.
• Apply mechanistic machine-learning concepts and OptiPrime-based prediction to compare candidate designs according to sequence features and predicted editing outcomes.

Day 3: Prime-Editing Optimisation, Candidate Ranking & Variant-Correction Design

• Compare multiple pegRNA candidates using predicted editing efficiency, sequence-context features, design-quality criteria and expected editing-outcome profiles.
• Evaluate desired versus unintended editing outcomes, PE3 and twinPE strategy differences, mismatch-repair-related considerations and other factors that may influence prime-editing performance.
• Apply multi-parameter candidate prioritisation, interpret AI prediction confidence and limitations, and translate computational predictions into a research-oriented therapeutic variant-correction strategy.

• Convert chemical composition and crystal-structure information into machine-learning-ready datasets.
• Generate composition- and structure-based descriptors using pymatgen and matminer.
• Build and evaluate AI/ML models for materials-property prediction.
• Apply Random Forest and XGBoost for regression, classification and candidate screening.
• Interpret model predictions using SHAP and feature-importance analysis.
• Identify key physicochemical and structural features influencing material properties.
• Perform high-throughput computational screening of candidate materials.
• Rank promising materials using predicted performance and multi-parameter criteria.
• Establish interpretable structure–property relationships using AI.
• Assess model performance, prediction confidence and limitations.
• Develop an end-to-end workflow for AI-driven materials and nanomaterials discovery.
• Generate a scientifically supported shortlist of materials for further computational or experimental validation.
• Prepare a final AI-Guided Materials Discovery & Candidate Screening Report.

• Nanoscience and Nanotechnology
• Materials Science
• Materials Engineering
• Chemistry
• Chemical Engineering
• Physics
• Applied Physics
• Metallurgical Engineering
• Ceramic Engineering
• Energy Materials
• Computational Materials Science
• Data Science and Artificial Intelligence
• Related interdisciplinary STEM disciplines