This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
Editorial Note: Multi-objective optimization using improved NSGA-II for integrated process planning and scheduling problems in a machining job shop for large-size valve. [PDF]
PLOS One Editors.
europepmc +1 more source
Rapidly Solidified High‐Strength Invar 36 Prepared by Planar‐Flow Melt Spinning
The Invar 36 alloy was rapidly solidified using the planar‐flow melt‐spinning technique. Ribbon samples with thicknesses ranging from 20 to 160 mm were produced. As the grain size of the ribbon decreased to sub‐micron levels, the hardness increased by more than 2 times.
Bekir Akgül, Mehmet Kul
wiley +1 more source
Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen +14 more
wiley +1 more source
The double-edged sword of rotational shift work: a qualitative study of worker perceptions in Saudi Arabia. [PDF]
Algarni MA, Maash OA.
europepmc +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery
We present FastNano Liquid, an automated small‐ and wide‐angle X‐ray scattering platform for the combined synthesis and characterization of (nano)materials. The platform is coupled to varied reactor workflows for both in situ studies of reaction kinetics and ex situ screening of synthesis conditions to support machine learning‐guided exploration ...
Pierre‐Baptiste Flandrin +16 more
wiley +1 more source
To enhance through‐thickness conductivity without sacrificing impregnation, large spherical graphite particles are intentionally employed in a low‐viscosity resin. Unlike finer conductive fillers, these particles remain outside the fiber bundles and accumulate in resin‐rich interlaminar regions during molding.
Keito Hosoe +6 more
wiley +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Multi-Strategy Enhanced White Shark Optimizer for Solving Job Shop Scheduling Problem. [PDF]
Cao L +5 more
europepmc +1 more source

