The workshop will focus on the recent progress in the development and application of chemically complex materials such as steels, superalloys and high-entropy alloys. The main topics of this (9th) ADIS workshop, will be digitalization and workflows, including machine learning approaches.
The aim of the workshop is to create a platform for materials scientists and software developers to discuss and exchange recent results and developments in the field of complex materials and workflows. The focus on digitalization and workflows reflects the fact that any design strategy for complex alloys such as steels 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.
The paradigm of a theory-guided data-driven materials research, is currently extending the traditional means of material science, which are based on fundamental principles and empirical wisdom from experiment, theory, or simulation. The challenge is to use 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. Recent success cases, which illustrate the synergy of condensed-matter physics, mechanical engineering and big-data informatics include combinatorial high-throughput screenings with density-functional theory and machine-learning approaches that exploit internet repositories of materials data.
Recently, there has also been progress towards the consideration of extended materials defects (grain boundaries, stacking faults, dislocation cores) and microstructures. This growing applicability of data-driven and physics-informed strategies which account for the multiscale character of novel materials shall be further encouraged by this Workshop. It intends to gather scientists who employ numerical simulations or related experiments for the high-throughput screening of materials and/or make use of machine learning approaches for analyzing large data sets in materials science. Submissions of contributions are solicited, which deal with developments of computational or experimental techniques for accumulating, analyzing, interpreting, storing, and sharing fundamental knowledge about materials in efficient ways in order to employ it for the design of novel iron- based and metallic materials. Contributions may range, and preferably bridge, from knowledge-driven research to application-oriented development.