Ab initio Description of Iron and Steel (ADIS2023):
Digitalization and Workflows
 

On-site workshop, 29 Oct - 03 Nov, 2023, Ringberg castle

The workshop will focus on the recent progress in the development and application of ab initio based methods for the description of chemically complex materials, such as steels, superalloys and high-entropy alloys. The main topics of the 9th ADIS workshop will be digitalization and workflows. It reflects the fact that any design strategy for these materials requires a combined multi-disciplinary effort. A wide array of approaches and algorithms needs to be developed, implemented and evaluated with respect to predictive power. In this context we also want to discuss the recent developments in the fields of data mining, machine learning, and artificial intelligence for the identification of structure-composition-property relationships in the high-dimensional materials data space.

Organizers

Dr. Tilmann Hickel
 

Group Computational Phase Studies &
BAM Federal Institute for Materials Research and Testing more

Prof. Dr. Jörg Neugebauer

Director &
Head of Department for Computational Materials Design more

Prof. Ralf Drautz

Head of Department Atomistic Modelling and Simulation &
Visiting Professor, University of Oxford more

Funding

The workshop is supported by

Psi-k Network

Psi-k is a Europe-based, worldwide network of researchers working on the advancement of first-principles computational materials science. Its mission is to develop fundamental theory, algorithms, and computer codes in order to understand, predict, and design materials properties and functions. more

National Research Data Infrastructure for Materials Science & Engineering.

To implement a material-specific data space, the NFDI-MatWerk aims to reduce technological barriers in MSE by developing generic software tools and an overarching data and information infrastructure. Among other aspects,  this will enable individual scientists to share tools and modularized workflows simultaneously for experimental, theoretical, and data-driven materials science. more