AI for materials science
Machine learning and knowledge-driven frameworks for materials discovery, laboratory intelligence, and interpretation of complex scientific data.
Künstliche Intelligenz / Materialdatenwissenschaft / Graph Learning
Prof. Dr. Mehrdad Jalali
Forschung zu künstlicher Intelligenz, Chemoinformatik, Graph Learning, Optimierung und Materialinformatik mit einem klaren Bezug zwischen methodischer Originalität und wissenschaftlicher Entdeckung.
Professor für Künstliche Intelligenz und Chemoinformatik
SRH University Heidelberg
150+
Published papers
35+
Courses taught
20+
Years in academia and research
29
Public GitHub repositories
Forschungsprofil
Wissenschaftliche KI für strukturierte Entdeckung
Das aktuelle Forschungsprogramm verbindet Materialwissenschaft, graphbasierte Lernverfahren, Optimierung, semantische Technologien und generative KI für wissenschaftliche Arbeitsabläufe.
Graph learning and scientific networks
Graph neural models, representative sampling, network sparsification, and relationship-aware prediction in structured scientific domains.
Optimization and intelligent search
Original metaheuristic algorithms inspired by natural and social systems for engineering design, materials search, and complex optimization.
LLM-enabled scientific workflows
Domain-aware use of large language models for electronic laboratory notebooks, knowledge extraction, documentation, and research automation.
Ausgewählte Beiträge
Projekte mit methodischer und wissenschaftlicher Reichweite
Frugal graph learning
Black Hole Strategy
A gravity-inspired representative sampling framework for metal-organic framework networks. It identifies structurally informative nodes so graph learning systems can operate with reduced labels while preserving relational patterns.
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Graph convolutional networks
MOFGalaxyNet
A graph convolutional framework for predicting guest accessibility in metal-organic frameworks through a relationship-aware view of materials.
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Social network optimization
SOCIAL
A centrality and influence-aware optimization algorithm that brings social network dynamics into metaheuristic search.
Read publicationAktuelle Publikationen
Ausgewählte Arbeiten
SOCIAL: Social Network Optimization Algorithm via Centrality and Influence-Aware Learning
Materials AI, Graph Learning & Optimization
MOF-LENS: Bio-Inspired Lotus Effect Optimization for Accelerated Discovery of Metal-Organic Framework Nanocarriers for Doxorubicin Delivery in Cancer Therapy
Materials AI, Graph Learning & Optimization
The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks
Materials AI, Graph Learning & Optimization
Application of Neural Networks for Advanced IR Spectroscopy Characterization of Ceria Catalysts Surfaces
Materials AI, Graph Learning & Optimization
Inverse Link Prediction with Graph Convolutional Networks for Knowledge-Preserving Sparsification in Cheminformatics
Materials AI, Graph Learning & Optimization
Large Language Models in Electronic Laboratory Notebooks: Transforming Materials Science Research Workflows
NLP, LLMs & Intelligent Systems