Results 131 to 140 of about 91,248 (260)

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Navigating Ternary Doping in Li-ion Cathodes With Closed-Loop Multi-Objective Bayesian Optimization. [PDF]

Adv Mater
Zeinali Galabi N, Liu CH, Jain M, Kamel M, Jia S, Bengio Y, McCalla E.   +6 more
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Closing the loop: human-augmented, mechanistically enhanced AI for proactive management of drug-drug interactions. [PDF]

Front Pharmacol
Spanakis M, De Pauw A, Brumer M, Symvoulakis EK, De Loof H.   +4 more
europepmc   +1 more source

Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy

Advanced Science, EarlyView.
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang, Shengxin Lu, Guangyong Zheng, Shensuo Li, Hongyu Chen, Mei Hong, Xiangru Zhou, Ruotian Tang, Ye Wu, Weidong Zhang, Dong Lu, Xin Luan   +11 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Computational optimization of DEK1 calpain domain solubility through integrated structural modelling and data-driven targeted mutagenesis. [PDF]

Sci Rep
Dabiri M, Levarski Z, Struhárňanská E, Demko V, Beneš V, Turňa J, Stuchlík S.   +6 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

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Multimorbidity: a core priority for learning health systems amidst vertical disease programme cuts. [PDF]

Health Res Policy Syst
Dixon J, Dhodho E, Webb K, Chimberengwa P, Mundoga F, Choga K, Chinyanga TT, Midzi N, Mudavanhu J, Nkala L, Gwati G, Gongora R, Zizhou S, Shambira G, Makurumidze R, Midzi SM, Abimbola S, Chandler CIR, Ferrand RA.   +18 more
europepmc   +1 more source