Communications of the ACM
Using Adversarial Attacks to Refine Molecular Energy Predictions
Adversarial attacks can improve the reliability of neural networks (NN) in predicting molecular energies by quantifying their uncertainty, according to a new report by Massachusetts Institute of Technology (MIT) researchers. The team used adversarial attacks to sample molecular geometries in a potential energy surface (PES) and tapped multiple NNs to forecast the PES from the same data.
"We aspire to have a model that is perfect in the regions we care about [i.e., the ones that the simulation will visit] without having had to run the full ML [machine learning] simulation, by making sure that we make it very good in high-likelihood regions where it isn't," said MIT's Rafael Gomez-Bombarelli.
From MIT News
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