Results 161 to 170 of about 168,693 (248)

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Optimization of ecological and efficient restoration technology for green mines based on hesitant fuzzy TOPSIS. [PDF]

Sci Rep
Wang B, Guo D, Sun J, Guo J, Gao S, Lu W.   +5 more
europepmc   +1 more source

Information Dense and Industry Scalable Accelerated Formation

Advanced Intelligent Discovery, EarlyView.
Pulsed formation can reduce lithium‐ion battery formation time by over 50% while maintaining or enhancing performance. Validated on 25 Ah prismatic cells, this industry‐scalable method yields thinner, more homogeneous solid electrolyte interphases (SEIs).
Leon Merker, Markus Blessing, Bojing Zhang, Helge Sören Stein   +3 more
wiley   +1 more source

Using physical model test and numerical simulation for revealing the mechanism of stope collapse: a case study. [PDF]

Sci Rep
Zhang R, Xie C, Chen J.
europepmc   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

Identification of Discharge Pathways of Acidic Wastewater from a Bauxite Mine (Lujiang Alum Mine, China) Before and After Artificial Disturbance. [PDF]

Toxics
Wang W, Jia W, Xu L, He Z, Kang B, Chen K.   +5 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Numerical modeling of coupled stress-fracture evolution in water-resisting key strata during longwall mining. [PDF]

Sci Rep
Gao H, Ji L, Huang Y, Li J.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Chaos-Enhanced, Optimization-Based Interpretable Classification Model and Performance Evaluation in Food Drying. [PDF]

Biomimetics (Basel)
Kaymak C, Alatas B, Yildirim S, Akpinar E, Katircioglu GG, Catalkaya M, Akay OE, Das M.   +7 more
europepmc   +1 more source