A physics-inspired evolutionary machine learning method: from the Schr\"odinger equation to an orbital-free-DFT kinetic energy functional

Abstract

We introduce a machine learning (ML) supervised model function that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Omega, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena the data are derived from. The fundamental equations of physics can be derived from this ML-Omega evolutionary method when provided the proper training data. By training the ML-Omega model function with only three hydrogen-like atom energies, the method can find Schr\"odinger's exact functional and, from it, Schr\"odinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula E = -0.7687Z^7/3, it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional Ts based on the gamma-TF-lambda-vW model. By considering the theoretical energies of only 5 atoms (He, Be, Ne, Mg, Ar) as the training set, the evolutionary ML-Omega method finds an ML-Omega-OF-DFT local Ts functional (gamma-TF-lambda-vW (0.964, 1/4)) that outperforms all the OF- DFT functionals of a representative group. Moreover, our ML-Omega-OF functional overcomes the LDA's and some local GGA-DFT's functionals' difficulty to describe the stretched bond region at the correct spin configuration of diatomic molecules. Although our evolutionary ML-Omega model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work we exploit prior-form functional expressions to make the training process faster in the example problems presented here.