Neural Operator cDFT: A New Paradigm for Molecular Modeling

Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework is rigorous, its practical application is often constrained by the challenge of formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this talk, I will discuss a novel approach to cDFT based on neural operators. These machine learning models can efficiently learn the complex functional relationships between the density profile, one-body direct correlation function, and external potential. By leveraging the power of neural operators, we can achieve high-precision cDFT calculations at a fraction of the computational cost of traditional methods.