Quantum mechanical molecular ‘fingerprints’ solve machine learning mystery

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's structural information: two hydrogen atoms flanking an oxygen atom with the bonds a certain length and a certain bond angle.

Or you could use the molecule's quantum mechanical information. That is, if you can package this complex information in a compact manner that is understandable to the ML algorithm. Cornell chemists have just discovered how. Their new method, Semi-Local Density Fingerprints (SLDFs), can predict molecular properties with up to 100 times more accuracy than the current most popular method for modeling molecules and materials.

In their quest to use ML to predict molecular properties, DiStasio, associate professor of chemistry and chemical biology in the College of Arts and Sciences, and members of his lab have found a way to encapsulate a molecule's quantum mechanical information so they can feed that - rather than simpler structural information like the identities and positions of the atoms - into ML algorithms, providing orders of magnitude greater accuracy than using only Density Functional Theory (DFT), the current most popular method.

DiStasio is the corresponding author of "Learning Molecular Conformational Energies Using Semi-Local Density Fingerprints," published in the Journal of Physical Chemistry Letters on Dec. 17.

Read the full story on the College of Arts and Sciences website.