Results 201 to 210 of about 348,999 (338)

StahlDigital: Ontology‐Based Workflows for the Steel Industry

open access: yesAdvanced Engineering Materials, Volume 27, Issue 8, April 2025.
The strength of the steel industry is based on the mastery of microstructure–property relationships. Digital workflows contribute to this aim by making the complexity of workflows reproducible and their execution user independent. The tools and workflows developed in the project StahlDigital as part of the German MaterialDigital initiative are ...
Franz Roters   +18 more
wiley   +1 more source

Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative

open access: yesAdvanced Engineering Materials, Volume 27, Issue 8, April 2025.
The MaterialDigital initiative drives the digital transformation of material science by promoting findable, accessible, interoperable, and reusable principles and enhancing data interoperability. This article explores the role of scientific workflows, highlights challenges in their adoption, and introduces the Workflow Store as a key tool for sharing ...
Simon Bekemeier   +37 more
wiley   +1 more source

Dimensionless Investigations on Energy Conversion and Analysis of Interlayer Time in Laser‐Based Powder Bed Fusion of Polymers for Polyamide 12 with Nanoadditives and Polypropylene

open access: yesAdvanced Engineering Materials, EarlyView.
This study explores the energy conversion in powder bed fusion of polymers using laser beam for polyamide 12 and polypropylene powders. It combines material and process data, using dimensionless parameters and numerical models, to enable the prediction of suitable printing parameters.
Christian Schlör   +9 more
wiley   +1 more source

Exploration of Chemical Space Through Automated Reasoning. [PDF]

open access: yesAngew Chem Int Ed Engl
Clymo J   +7 more
europepmc   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani   +4 more
wiley   +1 more source

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