Machine Learning for Materials
Mission and Research Topics
Quantum mechanics (QM) allows calculation of properties of molecules and materials, but accurate numerical procedures scale as high-order polynomials in system size, preventing applications to large systems, long time scales, or big data sets. Machine learning (ML) provides algorithms that identify non-linear relationships in large high-dimensional data sets via induction. Our research focuses on models that combine QM with ML.
These QM/ML models use ML to efficiently interpolate between QM reference calculations, yielding speed-ups of up to several orders of magnitude when the same QM procedure is carried out for a large number of similar inputs, e.g., in computational screenings, molecular dynamics, or self-consistent field calculations. We are particularly interested in models that generalize across different materials.