Results 241 to 250 of about 1,708,096 (323)
Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery
Machine learning based computational materials discovery workflows have recently proposed thousands of potentially stable crystalline materials. However, the experimental realization of these predictions is often challenging because the models assume perfectly ordered structures.
Konstantin S. Jakob +3 more
wiley +1 more source
Fog computing based cost optimization for university governance. [PDF]
Tian Q, Li G.
europepmc +1 more source
Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang +8 more
wiley +1 more source
PickMe: Sample Selection for Species Tree Reconstruction using Coalescent Weighted Quartets. [PDF]
Rusinko J +6 more
europepmc +1 more source
A new monolayer insulator, InO2, is synthesized by intercalating indium beneath patterned epitaxial graphene on SiC, followed by high‐temperature oxidation. This selective confinement yields large‐area, uniform InO2 with a wide bandgap of 4.1 eV. Upon intercalation, the EG/n‐SiC junction transitions from ohmic to Schottky, exhibiting a rectification ...
Furkan Turker +18 more
wiley +1 more source
A short-term load forecasting framework for air conditioning system based on model stacking. [PDF]
Liu T +5 more
europepmc +1 more source
Structure and Energetics of Chemically Functionalized Silicene: Combined Density Functional Theory and Machine Learning Approach. [PDF]
Wojciechowski P, Bobyk A, Krawiec M.
europepmc +1 more source
Dating the Bacterial Tree of Life Based on Ancient Symbiosis. [PDF]
Wang S, Luo H.
europepmc +1 more source
Optimal Designs for Discrete Choice Models Via Graph Laplacians. [PDF]
Röttger F, Kahle T, Schwabe R.
europepmc +1 more source

