Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
Pihlajamäki, A., Linja, J., Hämäläinen, J., Nieminen, P., Malola, S., Kärkkäinen, T., & Häkkinen, H. (2021). Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces. In ESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08 (pp. 529-534). ESANN. https://doi.org/10.14428/esann/2021.es2021-34
Päivämäärä
2021Oppiaine
Koulutusteknologia ja kognitiotiedeTietotekniikkaComputing Education ResearchTutkintokoulutusHuman and Machine based Intelligence in LearningNanoscience CenterLearning and Cognitive SciencesMathematical Information TechnologyComputing Education ResearchDegree EducationHuman and Machine based Intelligence in LearningNanoscience CenterTekijänoikeudet
© Authors, 2021
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.
Julkaisija
ESANNEmojulkaisun ISBN
978-2-87587-082-7Konferenssi
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKuuluu julkaisuun
ESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08Asiasanat
Alkuperäislähde
https://www.esann.org/sites/default/files/proceedings/2021/ES2021-34.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/101573464
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiaohjelma, SA; Profilointi, SALisätietoja rahoituksesta
This work was supported by Academy of Finland through the AIPSE research program with grant 315549 to H.H. and 315550 to T.K., through the Universities Profiling Actions with grant 311877 to T.K., and through H.H.’s Academy Professorship. Work was also supported by ”Antti ja Jenny Wihurin rahasto” via personal funding to A.P..Lisenssi
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