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
Aflibercept and Faricimab Equipotently Restore Endothelial Barrier Function. [PDF]
Strunz T +8 more
europepmc +1 more source
A Novel Approach to Estimate the Transition Temperature via Dynamic Nanoindentation
A new dynamic nanoindentation‐based method was developed that uses the stiffness ratio as an indicator of the elastic–plastic deformation contributions at different temperatures. The approach successfully identified transition temperatures in ferritic steel and distinguished them from continuously ductile austenitic steel.
Stefan Zeiler +4 more
wiley +1 more source
Fine-Scale Habitat Associations and Diel Activity Patterns of Camera-Detected Alpine Musk Deer (<i>Moschus chrysogaster</i>) in a High-Altitude Tibetan Landscape. [PDF]
Liu Z +7 more
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
Nuclear archaeology reassesses Heisenberg's last reactor experiment. [PDF]
Park PJ +5 more
europepmc +1 more source
A high interstital austenitic steel is additively manufactured and postdensified by HIP to eliminate gas porosity. Thereby, the loss of N and Mn in Ar HIP atmosphere is investigated. Further, HIP using N2 atmosphere is investigated which led to excessive nitride formation.
F. Großwendt +4 more
wiley +1 more source
Portable structured-light 3D volumetry for breast and flap assessment: Technical validation and workflow development. [PDF]
Chen Y +9 more
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
Knowledge and Attitudes Toward Attention-Deficit/Hyperactivity Disorder Among Physicians and Senior Medical Students in Jordan [Letter]. [PDF]
Majeed S, Chowdhury SH, Pandit M.
europepmc +1 more source

