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
Optogenetic Dissection of Sensory Neuron-Macrophage Interactions in Psoriasiform Skin Inflammation: Advances Based on In Vivo Calcium Imaging Techniques. [PDF]
Zheng P, Chen L.
europepmc +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
Cortical hemodynamic responses to acupuncture at Lianquan (CV23) in patients with post-stroke dysphagia: a functional near-infrared spectroscopy study. [PDF]
Chen H +6 more
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
Integrating 3D-printed sclero-corneal models into Rigid Gas Permeable contact lens training: A pilot study. [PDF]
Tolón Zardoya N +6 more
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
Bibliometric and knowledge-map analysis of research on robot-assisted vascular interventional surgery (2015-2025). [PDF]
Liu Y, Li S, Liu C.
europepmc +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
Microbe Profile: <i>Plasmodiophora brassicae</i>, the gall-forming protist behind clubroot disease. [PDF]
Calcaterra M +3 more
europepmc +1 more source

