marcel.science / gknet
Green-Kubo Thermal Conductivities with Message-Passing Neural Networks
Hello! 🖖 This is the website for an ongoing project to compute thermal conductivities with the Green-Kubo method and message-passing neural networks. There is no paper yet, but feel free to have a look at the APS talk linked below, and keep an eye on twitter.
- TitleGreen-Kubo Thermal Conductivities with Message-Passing Neural Networks
- AuthorsM.F. Langer, F. Knoop, C. Carbogno, M. Scheffler, and M. Rupp
Abstract Accurate, precise, and efficient computational access to thermal conductivities of known and novel materials is a challenging and urgent problem that concerns scientific understanding as well as industrial applications. The Green-Kubo (GK) method combined with first-principles calculations enables the accurate determination of thermal conductivities, even for strongly anharmonic materials (1), but its applicability is limited by the high computational cost of the long dynamics simulations required. Machine-learning potentials can reduce this cost by orders of magnitude. Message-passing neural networks (MPNNs) acting on a graph representation of a material are promising for this application due to their relational inductive bias and implicit long-range nature. They model interactions between atoms as multiple iterations of a short-range interaction, allowing information to propagate beyond local environments while avoiding the costly evaluation of explicit long-range interactions. We show how the GK method can be formulated and implemented using MPNNs, and benchmark their performance for zirconium dioxide, a strongly anharmonic material.