Scandium-Labs/Scandium-Dataset
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Scandium Labs
AI Research for Computational Materials Discovery
Scandium Labs is an independent AI research organization focused on developing machine learning systems for computational materials science. Our work combines graph neural networks, scientific machine learning, and physics-informed artificial intelligence to accelerate the discovery and screening of advanced materials.
Our mission is to reduce the computational cost of materials discovery by building models that complement first-principles simulations and enable scalable exploration of chemical and structural design spaces.
Research Areas
- Physics-Informed Machine Learning
- Graph Neural Networks
- Computational Materials Science
- Crystal Structure Representation Learning
- High-Throughput Materials Screening
- Battery Materials Discovery
- Scientific Machine Learning
- AI for Scientific Discovery
What We Build
Our work includes open research, machine learning models, datasets, and tools for the materials science community.
Current areas of development include:
- Crystal property prediction models
- Physics-constrained graph neural networks
- Materials screening pipelines
- Scientific datasets and benchmarks
- Research software and reproducible training frameworks
As our research progresses, models, datasets, and demonstration Spaces will be released through this organization.
Featured Research
PIGNet V2
Physics-Informed Graph Neural Networks for High-Throughput Crystalline Material Property Prediction
PIGNet V2 explores the integration of physical constraints into graph neural networks for predicting electronic and thermodynamic properties directly from crystal structures.
The project investigates multi-task learning for:
- Formation Energy
- Band Gap
- Thermodynamic Stability
The preprint serves as the initial proof of concept for the research direction that underpins Scandium Labs.
Open Science
We believe scientific progress benefits from transparency and reproducibility.
Whenever possible, we aim to release:
- Research papers
- Model checkpoints
- Training code
- Datasets
- Evaluation benchmarks
- Technical documentation
We welcome constructive feedback from researchers, students, and practitioners working in AI and materials science.
Vision
Scandium Labs is building AI systems that assist scientists in discovering the next generation of materials for energy storage, semiconductors, catalysis, and sustainable technologies.
Our long-term objective is to develop reliable, physics-aware machine learning systems that become practical tools for scientific research and industrial materials discovery.
Connect
Website
https://shamique-khan.vercel.app/startups/scandium-labs
GitHub
https://github.com/ScandiumLabs-in
https://www.linkedin.com/company/scandium-labs/
Founder
Shamique Khan
Founder and AI Researcher
Shamique's research focuses on graph neural networks, scientific machine learning, and physics-informed artificial intelligence for computational materials science. His work aims to bridge advances in modern AI with the scientific principles that govern materials discovery.
Scandium Labs is an independent research initiative dedicated to advancing artificial intelligence for scientific discovery.