Results 221 to 230 of about 283,214 (276)

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

Orbital Topology of Chiral Crystals for Orbitronics. [PDF]

Adv Mater
Hagiwara K, Chen YJ, Go D, Tan XL, Grytsiuk S, Yang KO, Shu GJ, Chien J, Shen YH, Huang XL, Cojocariu I, Feyer V, Lin MT, Blügel S, Schneider CM, Mokrousov Y, Tusche C.   +16 more
europepmc   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Polarization Conversion and Optical Meron Topologies in Anisotropic Epsilon-Near-Zero Metamaterials. [PDF]

ACS Photonics
Aita V, Zaleska A, Putley HJ, Zayats AV.
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

The Detailed Balance Property and Chemical Systems out of Equilibrium. [PDF]

Bull Math Biol
Franco E, Velázquez JJL.
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

A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity. [PDF]

Sci Rep
Wee J, Gong X, Tuschmann W, Xia K.
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Longitudinal Multi-Channel Focused Vortex and Vector Beams Generation by Quarter-Wave Plate Meta-Atom Metasurfaces. [PDF]

Nanomaterials (Basel)
Ma T, Zhao K, Gu M, Zhou H, Liu C, Cheng C, Dong Q, Ma L.   +7 more
europepmc   +1 more source