Resources

Core Books¶

1. Pattern Recognition and Machine Learning, C. M. Bishop.

2. Probabilistic Machine Learning: An Introduction, K. P. Murphy.

3. Deep Learning for Molecules & Materials, A. D. White.

4. The Matrix Cookbook

5. Gaussian Processes for Machine LearningOpen Access, C. E. Rasmussen, C. K. I. Williams

6. Scientific Python Lectures

Datasets to explore¶

• Materials Project (crystal structures and properties).

• OQMD (formation energies).

• OC20/OC22 (catalysis).

• QM9 (small molecules), PCQM4Mv2 (graph-based property prediction).

• AFLOW, NOMAD, or any in-house datasets with clear licenses.