README.md

# 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://scandium-labs.com

**GitHub**

https://github.com/ScandiumLabs-in

**LinkedIn**

https://www.linkedin.com/company/scandium-labs/

**Email**

team@scandium-labs.com

---

## 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.*