Results 171 to 180 of about 977,116 (306)

Measuring Housing Activeness from Multi-Source Big Data and Machine Learning

, 2021
Yang Zhou, Lirong Xue, Zhengyu Shi, Libo Wu, Jianqing Fan   +4 more
openalex   +1 more source

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

SCAFFOLD-AWARE MACHINE LEARNING–DOCKING PIPELINE FOR TYK2 INHIBITOR DISCOVERY WITH CALIBRATED PRIORITIZATION OF 32 ACTIVES INCLUDING DEUCRAVACITINIB

Spectrum of Engineering Sciences
openalex   +2 more sources

Transformer Learning in Sequence-Based Drug Design Depends on Compound Memorization and Similarity of Sequence-Compound Pairs. [PDF]

Mol Inform
Bajorath J.
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors to predictive potency. [PDF]

J Comput Aided Mol Des
Kaya AO.
europepmc   +1 more source

Low-Complexity Machine Learning Models for Active Noise Control in Nonlinear Systems

Miguel Ferrer, María de Diego, Alberto Gonzalez   +2 more
openalex   +1 more source

OCTOID: A Soft Robotic System Featuring Programmable Shape Morphing and Dynamic Structural Coloration

Advanced Functional Materials, EarlyView.
Inspired by octopuses, actuating legs based on soft materials are fabricated with programmed chiroptical properties and mechanical behaviors to achieve dynamic color modulation and reversible shape morphing, and these legs are developed into a modular OCTOID system.
Seung Hui Han, Seok‐In Lim, Ki‐Hyun Ryu, Sang Seok Lee, Dong‐Gue Kang, Jahyeon Koo, Kwang‐Un Jeong, Shin‐Hyun Kim, Dae‐Yoon Kim   +8 more
wiley   +1 more source

Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor. [PDF]

Sci Rep
Dupuyds P, Rayar A, Opuu V, Lescanne C, Wang S, Annereau JP, Nghe P, Briseño-Roa L.   +7 more
europepmc   +1 more source

Electron–Matter Interactions During Electron Beam Nanopatterning

Advanced Functional Materials, EarlyView.
This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima, Prathiba Thamizhavel, Taylor J. Woehl   +2 more
wiley   +1 more source