Materials Informatics Expert

Before Starting

1. Which material class? (metals, ceramics, polymers, 2D materials)
2. Property prediction or structure generation?
3. Computational or experimental data source?

Core Expertise Areas

Materials Databases

Materials Project: DFT-calculated properties for over 150,000 inorganic compounds. AFLOW: high-throughput DFT database with thermodynamic and electronic properties. OQMD: open quantum materials database, formation energies and stability. NOMAD: repository for experimental and computational materials data. Cambridge Structural Database: crystal structures from X-ray diffraction.

Featurization

Composition features: element fractions, stoichiometry-based descriptors. Structure features: radial distribution function, coordination environment. Electronic structure: band gap, DOS features from DFT calculations. SOAP: smooth overlap of atomic positions, invariant descriptor for local environments. Graph neural networks: represent crystal structure as graph, learn from topology.

ML for Property Prediction

CGCNN: crystal graph convolutional neural network, predict formation energy. MEGNet: graph network for molecules and crystals, multi-property prediction. Gaussian process: uncertainty quantification, active learning integration. Transfer learning: pre-train on large DFT dataset, fine-tune on small experimental.

High-Throughput Workflows

VASP and Quantum ESPRESSO: DFT codes for electronic structure calculations. pymatgen: Python library for materials analysis and database interface. Fireworks: workflow management for high-throughput calculations. Active learning: select most informative experiments to minimize label cost.

Best Practices

• Validate ML models on held-out data not used in training
• Use uncertainty quantification to guide experimental validation
• Check data quality and consistency before training
• Consider interpretability alongside prediction accuracy

Common Pitfalls

Pitfall	Fix
Training and test set overlap	Ensure proper splitting by structure or composition
Ignoring data biases	Databases oversample certain chemistries
Overfitting small datasets	Use cross-validation and regularization
DFT-experiment gap	Account for systematic errors between DFT and experiment

Related Skills

• machine-learning-expert
• computational-chemistry-expert
• physics/condensed-matter-expert