A new SFB 1313 publication is published in "Chemical Engineering Science". The work has been developed by researchers involved in Project A01.
Authors
- Rolf Stierle (University of Stuttgart)
- Gernot Bauer (University of Stuttgart)
- Nadine Thiele (University of Stuttgart)
- Benjamin Bursik (University of Stuttgart, research project A01)
- Philipp Rehner (ETH Zürich)
- Joachim Gross (University of Stuttgart, research project A01)
Abstract
We show how classical density functional theory can greatly benefit from algorithmic advances in machine learning, especially neural networks. By exploiting GPU-accelerated backward automatic differentiation, we overcome the often cumbersome and error-prone implementation of functional derivatives for classical density functional theory computations. This provides an efficient and straightforward solution for computing functional derivatives, opening up a wide range of applications. We show the gain in computational performance by using backward automatic differentiation to compute the functional derivatives on GPUs, and exemplify the use of this easy-to-implement and highly extensible classical density functional theory framework to predict the adsorption isotherms of a methane/ethane mixture described by a Helmholtz energy functional based on the PC-SAFT equation of state in the covalent-organic framework 2,3-DhaTph. Together with this manuscript, we provide the full classical density functional theory code as supplementary material.