Dresden 2026 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 13: Data-driven Materials Science: Big Data and Workflows I
MM 13.5: Talk
Tuesday, March 10, 2026, 11:30–11:45, SCH/A251
Towards Disorder-Aware Materials Discovery - Recognizing and Modeling Crystallographic Disorder — •Konstantin S. Jakob1, Aron Walsh2, Karsten Reuter1, and Johannes T. Margraf1,3 — 1Fritz-Haber-Institut der MPG, Berlin — 2Imperial College London — 3Universität Bayreuth
Recent computational materials discovery efforts have led to an enormous number of predictions of previously unknown, potentially stable inorganic, crystalline materials. However, these efforts are currently limited to predicting perfectly crystalline materials. As a consequence, many of these predictions cannot be verified in experiments, where kinetic effects, defects, and crystallographic disorder can be crucial. Here, we discuss disorder as a current frontier in materials discovery. To this end, we show that machine learning classification models can reliably recognize disordered materials and demonstrate that a significant fraction of computationally predicted materials are likely disordered [1]. On the example of compositionally complex transition metal ferrite spinels, we then demonstrate how machine learning interatomic potentials and Monte Carlo sampling can be used to tackle such disordered systems efficiently.
[1] K.S. Jakob, A. Walsh, K. Reuter, and J.T. Margraf, Adv. Mater. e14226 (2025).
Keywords: Materials Discovery; Crystal Disorder; Machine Learning; Language Models