SCIENCE@home

Fast approximations by machine learning: predicting the energy of dimers using convolutional neural networks

D. Hennessey,M. Klobukowski,P. Lu

Published 2019 in THE EUROPEAN MODELING AND SIMULATION SYMPOSIUM

ABSTRACT

We introduce fast approximations by machine learning (FAML) to compute the energy of molecular systems. FAML can be six times faster than a traditional quantum chemistry approach for molecular geometry optimisation, at least for a simple dimer. Hardware accelerators for machine learning (ML) can further improve FAML’s performance. Since the quantum chemistry calculations show poor algorithmic scaling, faster methods that produce a similar level of accuracy to the more rigorous level of quantum theory are important. As a FAML proof-of-concept, we use a convolutional neural network (CNN) to make energy predictions on the F 2 molecular dimer system. Training data for the CNN is computed using a quantum chemistry application (i.e., GAMESS) and represented as an image. Using five-fold cross-validation, we find that the predictions made by the CNN provide a good prediction to the theoretical calculations in a fraction of the time.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-15 of 15 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1