6 papers · 1 filter
From Word2Vec to Transformers: Text-Derived Composition Embeddings for Filtering Combinatorial Electrocatalysts
Lei Zhang, Markus Stricker
Compositionally complex solid solution electrocatalysts span vast composition spaces, and even one materials system can contain more candidate compositions than can be measured exh…
Multi-modal data-driven microstructure characterization
Qi Zhang, Santiago Benito, Sebastian Weber +1
Electron backscatter diffraction is one of the most prevalent techniques used for microstructural characterization. In recent years, there has been an increase in the use of data-d…
Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon +3
Reproducibility of computational results remains a challenge in materials science, as simulation workflows and parameters are often reported only in unstructured text and tables. W…
Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations
Doaa Mohamed, Samuel GarcÃa Vázquez, Behnam Mardani +4
The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally compl…
Electrocatalyst discovery through text mining and multi-objective optimization
Lei Zhang, Markus Stricker
The discovery and optimization of high-performance materials is the basis for advancing energy conversion technologies. To understand composition-property relationships, all availa…
Composition-property extrapolation for compositionally complex solid solutions based on word embeddings
Lei Zhang, Lars Banko, Wolfgang Schuhmann +2
Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for…