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
Morphology, Transport, and Dynamics of Protein Adsorption in Open‐Cell Metal Foam
Stainless steel (SS) open‐cell foams are shown to adsorb more protein per unit area than previously reported 316L SS and chromium oxide surfaces under static and flow conditions. An integrated approach combining 3D pore imaging, flow simulation, and protein adsorption experiments characterizes the foam’s performance.
Chinmaya Prerana Inguva +2 more
wiley +1 more source
Long-Term Outcomes of Indocyanine Green Versus Inflation-Deflation for Intersegmental Plane Identification in Segmentectomy for Early-Stage Lung Cancer. [PDF]
Bae SY +10 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
Data-driven machine learning optimisation of silver nanoparticle synthesis. [PDF]
Carver J +3 more
europepmc +1 more source
Effects of Mg on Microstructure and Solidification of a Hypereutectic Zn–8 wt.%Al Alloy
An appreciable set of results involving thermal data, microstructure, chemical composition, and microstructural growth laws is reported for ZnAlMg alloys. Such results demonstrate that ZnAlMg alloys have high potential for applications in automotive self‐lubricating components, batteries, and electrical systems.
Raí B. de Sousa +6 more
wiley +1 more source
Inverse-Probability-Weighted Kernel Estimation of Regression Derivatives Under Missing-at-Random Responses for Stationary Ergodic Processes. [PDF]
Bouzebda S, Didi S.
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
Geochemical Effects of Groundwater Interaction with Steel Slag Aggregate Used in Road Construction. [PDF]
Adamczyk Z +3 more
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

