Capturing weak interactions in surface adsorbate systems with graph-theoretic fragmentation and machine learning at coupled cluster accuracy: a study of perfluorooctanoic acid on the water surface

Environmental studies found that acidic polyfluorinated alkyl substances, especially Perfluorooctanoic acid (PFOA), had spread throughout the environment and bioaccumulated into human populations residing near contaminated watersheds, leading to systemic maladies such as thyroid and kidney diseases, a range of cancers, and infertility. Thus the study of the interactions of PFOA with water surface becomes important for the mitigation of their activity as pollutants and threats to public health. However, theoretical study of the interactions of such organic adsorbates on the surface of water, and their bulk concerted properties, often necessitates the use of ab initio methods to properly incorporate the long range electronic properties that govern these extended systems. Notable theoretical treatments of “on-water” reactions thus far have employed hybrid DFT and semilocal DFT, but the interactions involved are weak interactions that may be best described using post-Hartree-Fock theory. In this work, we aim to demonstrate a graph-theoretic approach that accurately captures both the critical “weak” interactions while maintaining an efficient ab initio treatment of the long range, periodic interactions which underpin the physics of extended systems. Here, we apply the above graph-theoretical methodology to study a PFOA on the surface of water as a model system for the study of weak interactions seen in the wide range of surface interactions and reactions.

The graph-theoretic approach divides a system into a set of nodes, or vertices, that are then connected through edges, faces, and higher order graph theoretic objects known as simplexes, to represent locally interacting sub-systems. Each such sub-system is used to construct molecular dynamics calculations and compute multi-dimensional potential energy surfaces. Recently, based on our graph-theory ideas, we introduced a new transfer learning procedure to construct the full system potential energy from a family of neural networks for each type of simplexes. We use a unique multi-dimensional clustering algorithm to determine our training data for machine learning models. These models are then used to extrapolate the energies for molecular dynamics trajectories at less than one-tenth the cost as compared to a regular fragmentation-based dynamics calculation with an excellent agreement with a couple cluster level of full system potential energies.