6533b85afe1ef96bd12b983f
RESEARCH PRODUCT
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
Hannu HäkkinenSami MalolaAntti PihlajamäkiJoakim LinjaTommi KärkkäinenJoonas HämäläinenPaavo Nieminensubject
atomsComputer sciencebusiness.industryforce directionsmolekyylitOrientation (graph theory)nanotieteetatomitmachine learningkoneoppiminenMinimal learning machineComputer visionmoleculesArtificial intelligencebusinessdescription
Machine learning (ML) force fields are one of the most common applications of ML in nanoscience. However, commonly these methods are trained on potential energies of atomic systems and force vectors are omitted. Here we present a ML framework, which tackles the greatest difficulty on using forces in ML: accurate prediction of force direction. We use the idea of Minimal Learning Machine to device a method which can adapt to the orientation of an atomic environment to estimate the directions of force vectors. The method was tested with linear alkane molecules. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |