DFT Literacy: Structured Notes on Density Functional Theory for ML Researchers
Published:
A structured set of learning notes on Density Functional Theory (DFT), designed for ML/CS researchers who are new to computational materials science.
The notes follow a “simplification chain” approach — tracing the 12-step journey from the many-body Schrödinger equation to a practical Quantum ESPRESSO input file. Each concept is anchored to its corresponding QE parameter and DFTBench evaluation field, with ML analogies throughout.
Contents: 20 source notes + 19 Q&A documents covering quantum mechanics basics, Born-Oppenheimer approximation, Kohn-Sham equations, pseudopotentials, plane-wave basis sets, k-point sampling, SCF iteration, structural relaxation, output observables, and material categories.