Marx Group
Active learning for machine learning potentials of aqueous solutions
Please discuss your project with a possible supervisor Prof. Dr. Dominik Marx before you fill out the application. Email to Dr. Nore Stolte: nore.stolte@theochem.ruhr-uni-bochum.de
About the group and the topic
The general theme of our research consists in understanding structure, dynamics, and chemical reactions of complex molecular many-body systems. Our aim is to capture nature as closely as possible by theoretical means. Our most recent methodological developments focus on computationally highly efficient (vulgo “cheap”) techniques that allow us to automatically construct highly accurate machine learning potential energy and electronic property surfaces which are exclusively based on the most accurate electronic structure methods currently available, namely on CCSD(T) coupled cluster calculations. Students that join our group will have the opportunity to use and advance cutting-edge methods in the field of machine learning applied to molecular dynamics simulations.
A machine learning (ML) potential energy surface at the “gold standard” CCSD(T) coupled cluster accuracy level for the liquid phase has been produced by our group. However, including the description of solutes requires new coupled cluster calculations, and re-training of the machine learning model. We are interested in investigating how to reduce the computational cost for this problem.
What they expect from students
It is desirable that students have experience with molecular dynamics simulations and/or experience with electronic structure calculations (DFT/MP2/etc.). Students must be comfortable using the command line in a Unix environment, and have at least basic programming/scripting experience (python/bash/Fortran/etc.).
Papers related to the topic
Win a research stay
Subject: enact your research proposal
Duration: 3 months
Location: Ruhr University Bochum, Technical University Dortmund,University Duisburg-Essen, Max-Planck-Institut
Language of instruction: English
Funding: RESOLV sponsors travel (reimbursement) and a monthly stipend
Period of research stay: before December 2027