Details

Autor(en) / Beteiligte

• Jacobsen, T L
• Jørgensen, M S
• Hammer, B

Titel

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

Ist Teil von

• Physical review letters, 2018-01, Vol.120 (2), p.026102-026102, Article 026102

Ort / Verlag

United States

Erscheinungsjahr

2018

Quelle

American Physical Society Journals

Beschreibungen/Notizen

• Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO_{2}(110)-(4×1) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.