Results 161 to 170 of about 168,133,245 (277)

Correction to "A Practical Start-Up Guide for Synthetic Chemists to Implement Design of Experiments (DoE)". [PDF]

ACS Catal
Wall BJ, Koeritz MT, Stanley LM, VanVeller B.   +3 more
europepmc   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Formulation and Optimization of a <i>Melissa officinalis</i>-Loaded Nanoemulgel for Anti-Inflammatory Therapy Using Design of Experiments (DoE). [PDF]

Gels
Koushik Y, Rama Rao N, Sri Venkatesh U, Rami Reddy GV, Surendra AV, Sreenu T.   +5 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Aromatic Pummerer‐Promoted Stereospecific Cross‐Coupling of Thiazoles With Boronic Esters

Angewandte Chemie, EarlyView.
A lithiation–borylation–aromatic Pummerer (LiBAP) strategy enables stereospecific, C5‐selective C(sp2)–C(sp3) cross‐coupling of thiazoles with boronic esters. By using exocyclic sulfoxide activation to overcome the low nucleophilicity that frustrates SEAr‐type coupling, the method delivers enantioenriched alkylated thiazoles bearing a versatile C2 ...
Mark John P. Mandigma, Julien Lefranc, Louise Eagling, Jasper L. Tyler, Varinder K. Aggarwal   +4 more
wiley   +2 more sources

AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models. [PDF]

Sci Rep
Xu X, Li D, Bi J, Moeckel M.
europepmc   +1 more source

Automated Bacterial Identification and Morphological Feature Analysis in Low‐Dose Cryo‐EM Using YOLOv11

Advanced Intelligent Discovery, EarlyView.
AI‐based tools enable rapid characterization of bacterial ultrastructure in low‐dose cryogenic transmission electron microscopy. The envelope thickness tool quantifies membrane thickness and anisotropy. The flagella module analyzes filament morphology and detects cell‐flagella contacts.
Sita Sirisha Madugula, Lynnicia N. Massenburg, Spenser R. Brown, Amber N. Bible, Chanda R. Harris, Lance X. Zhang, Kiara Parker, Scott T. Retterer, Jennifer L. Morrell‐Falvey, Rama K. Vasudevan, Alexis N. Williams   +10 more
wiley   +1 more source

Hydrogen‐Bonding Order Associated With Radical Delocalization in Layered Organic Cathode Materials

Angewandte Chemie, EarlyView.
“TAQ” and TAPT share the same pyrazine‐based molecular structure but differ in degree of crystallinity. Combined x‐ray scattering, solid‐state NMR, MicroED, and EPR reveal that “TAQ”'s higher crystallinity and ordered hydrogen‐bonding network are associated with enhanced radical delocalization and conductivity, highlighting structural order as a design
Alae Eddine Lakraychi, Zeqian Zhang, Oliver Lin, Eric D Walter, Jiaqi Wang, Harshan Reddy Gopidi, Eliot F. Woods, Ruozhu Feng, Wei Wang, Pieremanuele Canepa, Brett A. Helms, Kamila M. Wiaderek, Qian Chen, Yan Yao, Ying Chen   +14 more
wiley   +2 more sources