Our vision:
The vision is to be able to make accurate computational predictions for material properties that are both difficult to access both experimentally and theoretically. To this end, we will design and employ novel methods combining statistical mechanics, first-principles methods, and machine learning. We want to be rigorous and innovative.
"The fundamental laws necessary for the mathematical treatment of a large part of physics and the whole of chemistry are thus completely known, and the difficulty lies only in the fact that application of these laws leads to equations that are too complex to be solved. "
“...approximate practical methods of applying quantum mechanics should be developed, which can lead to an explanation of the main features of complex atomic systems without too much computation.” Paul Dirac (6 April 1929)
To combine the accuracy of the first principles methods and efficiency of the empirical force fields, we can exploit machine learning (ML) techniques to “learn” the atomic interactions from quantum mechanics.
In essence, a ML interatomic potential “remembers” a pre-computed database of reference structure-property pairs, and makes predictions about a new structure by comparing it to the members of the reference set.
By marrying advanced statistical mechanics methods with data-driven machine learning interatomic potentials, we want to develop and apply a method to predict the behavior of materials at finite temperatures using first principles methods based on quantum chemistry.
