Results 221 to 230 of about 203,799 (303)

Mn‐Substituted Topological Phase Transition in Topological Insulator Pt2HgSe3

Advanced Electronic Materials, EarlyView.
ABSTRACT Topological materials have gained a significant interest in recent years due to their unique features, leading to topological insulators (TI), Dirac or Weyl semimetals, etc. The layered material Pt2HgSe3${\rm Pt}_2{\rm HgSe}_3$ is found to be a dual TI characterized by significant spin–orbit coupling and band inversion.
Deergh Bahadur Shahi, Gang Bahadur Acharya, Dipak Bhattarai, Bishnu Karki, Madhav Prasad Ghimire   +4 more
wiley   +1 more source

Multimodal Deep Learning for Humphrey 24-2 Visual Field Prediction: Evaluating the Contribution of Fellow-Eye Information in Glaucoma and Ocular Hypertension. [PDF]

J Clin Med
Firat M, Tuncer Firat I, Erbali H, Ozturk E, Tuncer T.   +4 more
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Entry Point Localisation for Percutaneous Surgical Robots Based on Concentric Fiducial Patches and Robust Surface Fitting. [PDF]

Int J Med Robot
Deng Y, Jiang Q, Wang J, Tang J.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

First-Principles Study on the Magnetic Properties of Monolayer MOCl (M = Ti, V, Cr, Mo). [PDF]

Nanomaterials (Basel)
Pan Y, Wang Y.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

High-precision temperature-strain composite sensing based on a single resonator-type surface acoustic wave sensor using machine learning algorithms. [PDF]

Microsyst Nanoeng
Cheng C, Liu Y, Yang J, Luo T, Tong T, Luo C, Li X, Fang X, Ke Q.   +8 more
europepmc   +1 more source