Development of a surrogate machine learning model for the acceleration of density functional calculations with the Korringa-Kohn-Rostoker method

Density functional theory (DFT) has become an indispensable tool in materials science. Specialized DFT methods like the Korringa-Kohan Rostoker Green Function (KKR) method are predestined to investigate the technologically relevant effects of crystallographic defects on the electronic and magnetic structure of host materials. This thesis lays the groundwork for answering the question of whether surrogate machine learning (ML) models have the potential to accelerate such DFT calculations since their computational complexity severely limits them to systems sizes of about a thousand atoms in practice. To that end, a versatile suite of software tools that facilitates the generation and analysis of high-throughput computing DFT datasets with the JuKKR DFT codes and the AiiDA workflow engine is presented. We demonstrate its use by generating a database of 8,760 converged KKR DFT calculations of single impurity embeddings into elemental crystals with 60 different chemical elements and varying lattice constants and that preserves the full data provenance of each calculation. Finally, we use the single-impurity database to compare the Coulomb Matrix and the Smooth Overlap of Atomic Positions (SOAP) as structural descriptors of the local atomic environment for materials defects. Their potential use in surrogate ML models is showcased in a simple example of host crystal structure prediction that achieves 93 percent accuracy

SiSc Lab 2022, Project 6. A machine learning playground in quantum mechanical simulation.

Density functional theory (DFT) is one of the most widely used simulation techniques. About a third of world supercomputing time is spent each year on such calculations. DFT approximates the solution to the Schrödinger equation, to elucidate the electronic structure of materials and molecules. While it makes quantum many-body problems in a lot of systems of interest tractable, it still is computationally demanding. It typically scales in O(N^3) with the number of electrons in the system, limiting its application to systems with a few thousand atoms at most. Over the last 15 years, the development of surrogate models based on machine learning (ML) has steadily gained momentum in the field of atomistic simulation. In ab initio molecular dynamics for instance, machine-learned interatomic potentials at a fraction of the cost and comparable accuracy of mechanistic methods have already become mainstream. Now these surrogate models also start to increasingly be developed to predict the underlying electronic structure properties of atomic systems directly.In this project, the students will be given the chance to play around with a wide array of state-of-the-art models in this field, from traditional kernel methods to deep graph convolution networks. They will be provided with a computational infrastructure and training datasets from DFT calculations. The challenges ladder they will climb has the rungs a) understanding the electronic structure data, b) understanding the reasoning behind the various surrogate ML modeling approaches, c) discovering common features of the atomic systems to come up with clever optimizations of the model architectures, and d) achieving reasonable prediction accuracy for the targeted electronic structure properties. The project goals will be adjusted, in a reasonable range given the limited time frame, according to the speed of progress and the particular interests of the students.Expected prerequisites: Applied Quantum Mechanics, basic Python skills. Desired, but optional: Physics track, some hands-on ML experience.Advisors : Johannes Wasmer, Philipp Rüßmann , Stefan Blüge

Best of Atomistic Machine Learning

A ranked list of awesome atomistic machine learning projects

JuDFTteam/aiida-jutools

<p>Tools for simplifying daily work with the AiiDA workflow engine.</p&gt

AiiDA-JuTools

Tools for simplifying daily work with the AiiDA workflow engineIf you use this software, please cite it using the metadata from this file

JuDFTteam/aiida-jutools

Tools for simplifying daily work with the AiiDA workflow engine

Comparison of structural representations for machine learning-enhanced DFT of impurity embeddings

The acceleration or even replacement of ab initio methods for atomistic systems with surrogate models based on machine learning has gained traction in recent years [1]. This development stands on two pillars: The first one is the fast growth of materials databases, thanks in part to high-throughput calculation (HTC) infrastructures such as AiiDA [2]. The second one is advances in method development in atomistic machine learning, where finding the best representation of an atomic system as input for model training has been identified as a crucial step to success. Structural representations rely, like the Schrödinger equation, only on the atom positions and their chemical identity within a system [3], and are thus most suitable for this task.Here we investigate the possibility to accelerate the density functional theory (DFT) code juKKR [4] with machine learning starting potentials. This code has been used for instance to perform HTC on impurity embeddings into topological insulators [5]. We use a combinatorial approach to generate 7000 impurity embeddings from most elements of the periodic table into elemental crystals with the help of AiiDA. We generate their fingerprints using structural descriptors implemented in the DScribe package [6], such as smooth overlap of atomic positions. To benchmark their representational power for these embeddings, we present the results of a simple classification experiment.We acknowledge support by the Joint Lab Virtual Materials Design (JL-VMD) and thank for computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer CLAIX at RWTH Aachen University. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – Cluster of Excellence Matter and Light for Quantum Computing (ML4Q) EXC 2004/1 – 390534769, and by AIDAS2 – AI, Data Analytics and Scalable Simulation – a virtual lab between CEA, France and FZJ, Germany

Best of Atomistic Machine Learning

A ranked list of awesome atomistic machine learning projects.If you use this dataset, please cite it using the metadata from this file