Results 171 to 180 of about 126,625 (241)

Sequential topological complexity of aspherical spaces and sectional categories of subgroup inclusions. [PDF]

Math Ann
Espinosa Baro A, Farber M, Mescher S, Oprea J.   +3 more
europepmc   +1 more source

Oxygen‐Driven Structural Reorganization by Trace Water Enabling Fast Li‐Ion Transport in a Pliable Solid Electrolyte

Advanced Energy Materials, EarlyView.
Trace water is acting as a constructive mediator in 2LiCl–GaF3, markedly increasing ionic conductivity while reorganizing local coordination. Hydration creates localized Li+ solvation environments and partially dissociates ion pairs, enhancing Li‐ion mobility.
Youngkyung Kim, Jae‐Seung Kim, Kwang Ho Shin, Jingyu Choi, Beom Jin Park, Jinyeong Choe, Jiwon Seo, Chanhyun Park, Jeongjae Lee, Dong‐Hwa Seo, Sung‐Kyun Jung   +10 more
wiley   +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

Machine learning predictions from unpredictable chaos. [PDF]

J R Soc Interface
Jiang J, Chen L, Ke L, Dou B, Zhu Y, Shi Y, Qiu H, Zhang BG, Zhou T, Wei GW.   +9 more
europepmc   +1 more source

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

Nonlocality-enabled photonic analogies of parallel spaces, wormholes and multiple realities. [PDF]

Nat Commun
Song T, Jing Y, Shen C, Chu H, Luo J, Jia R, Wang C, Xiao M, Zhang ZQ, Peng R, Wang M, Chan CT, Lai Y.   +12 more
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

A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences. [PDF]

J Chem Inf Model
Wee J, Jiang J.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source