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
Proportionate Flow Shop Games [PDF]
In a proportionate flow shop problem several jobs have to be processed through a fixed sequence of machines and the processing time of each job is equal on all machines.By identifying jobs with agents, whose costs linearly depend on the completion time ...
Borm, P.E.M. +3 more
core
Knowledge-driven teaching-learning-based optimization algorithm for bi-objective flexible job-shop scheduling problem with tool allocation. [PDF]
Chen K, Yuan X, Tan W.
europepmc +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
A hybrid differential evolution algorithm for distributed assembly flexible job shop scheduling with batch delivery and inventory. [PDF]
Zhou S, Han S, Yang M, Du B.
europepmc +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
Applying Machine Based Decomposition in 2-Machine Flow Shops [PDF]
The Shifting Bottleneck (SB) heuristic is among the most successful approximation methods for solving the Job Shop problem. It is essentially a machine based decomposition procedure where a series of One Machine Sequencing Problems (OMSPs) are solved ...
Chatterjee Ashis K, Mukherjee, Saral
core
Offline reinforcement learning for learning to dispatch for job shop scheduling. [PDF]
Remmerden JV, Bukhsh Z, Zhang Y.
europepmc +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
A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling. [PDF]
Li J, Li S, He P, Li H.
europepmc +1 more source

