Machine-learned surrogate models are used to find minima, saddle points and reaction paths on potential energy surfaces of materials. These are used to characterize the energetics of chemical processes or the structure of materials on an atomistic scale. We build machine-learned models on-the-fly as more and more data from electronic structure calculations become available. Optimization algorithms are developed and adjusted to different ML approaches to deal with the inherent numerical noise in the data. The re-use of data as well as the combination of data sources with different cost and accuracy increases the computational efficiency. The goal is to gain as much knowledge about stationary points and optimal paths on the potential energy surface with as few electronic structure calculations as possible, since the latter are the computational bottleneck.
|Project Number||PN 3-4|
|Project Name||Characterization of potential energy surfaces using machinge-learning techniques|
|Project Duration||June 2019 - November 2022|
|Project Leader||Johannes Kästner
|Project Members||1 PhD Researcher|
|Project Partners||Christian Holm (PN 3-3): Atomic Descriptors for Machine Learning.
PN 5-7: Gaussian Processes
Ingo Steinwart (PN 6-3): Implementing Physical Constraints in Machine Learning