Neural Nets to Simulate Molecular Motion

Artificial neural nets can be trained to encode quantum mechanical laws to describe the motions of molecules, supercharging simulations potentially across a broad range of fields.

New deep learning models predict the interactions between atoms in organic molecules. These models will help computational biologists and drug development researchers understand and treat disease.

Quantum mechanical (QM) algorithms, used on classical computers, can accurately describe the mechanical motions of a compound in its operational environment. But QM scales very poorly with varying molecular sizes, severely limiting the scope of possible simulations. Even a slight increase in molecular size within a simulation can dramatically increase the computational burden. So practitioners often resort to using empirical information, which describes the motion of atoms in terms of classical physics and Newton’s Laws, enabling simulations that scale to billions of atoms or millions of chemical compounds.

SOURCES – Las Alamos National Lab
Written By Alvin Wang,

3 thoughts on “Neural Nets to Simulate Molecular Motion”

  1. Interesting. FTA:

    … the Los Alamos team, with the University of North Carolina at Chapel Hill and University of Florida, has developed a machine learning approach called transfer learning that lets them build empirical potentials by learning from data collected about millions of other compounds.

    So it would appear that they are training their nets with empirical data and not using the ‘quantum laws’.

    Paging Randall Mills….

Comments are closed.