Results 31 to 40 of about 8,300 (176)
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu +4 more
wiley +1 more source
A Language‐Guided Multimodal Foundation Model for Zero‐Shot and Multi‐Task Brain Signal Analysis
METIS aligns brain signals with natural‐language instructions to enable zero‐shot and multi‐task brain signal analysis. Pretrained on over 70 000 h of EEG and iEEG recordings, it generalizes across sleep stage classification, epilepsy detection, and neurological disorder diagnosis, providing a scalable foundation model for clinically meaningful brain ...
Mingzhi Chen +3 more
wiley +1 more source
The method presented in this paper provides a practical sensorless solution for estimating both contact force and contact location using only standard joint encoders and an available robot dynamic model. By reformulating the contact estimation problem using a single scalar equivalent contact parameter, the approach enables fast and robust computation ...
Thanh‐Quan Ta, Shyh‐Leh Chen
wiley +1 more source
Machine Learning Paradigm for Advanced Battery Electrolyte Development
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su +4 more
wiley +1 more source
From Data to Dimers: Engineering Acene Derivatives for Photovoltaic Singlet Fission
Excited‐state energies of acene derivatives are predicted using a ChemBERTa‐based regression model and subsequently used to screen candidates for photovoltaic singlet fission using dimer–monomer benchmarked energetic criteria. Promising candidates are predominantly 5‐ and 6‐fused heteroacenes, which offer enhanced stability and higher triplet energies ...
Alexander J. Cross +2 more
wiley +1 more source
Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation
Abstract Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO.
Qinqin Fan +3 more
wiley +1 more source
Revisiting Fisher's n‐D statistical vision: From algebraic abstraction to modern visualization
Abstract We revisit early foundational results in mathematical statistics derived by Ronald A. Fisher. They involve sampling distributions of statistics calculated from independent and identically distributed Normal observations, namely the root mean square deviation; the mean absolute deviation, conditional on already knowing the value of the root ...
James A. Hanley
wiley +1 more source
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang +6 more
wiley +1 more source
Cross‐Entropy of Power Spectral Density Function: A Modal Identification Framework
ABSTRACT The power spectral density (PSD) function of measured structural response contains a significant amount of information, including the modal parameters (natural frequencies, damping ratios). Output‐only system identification or modal identification technique can be used for extracting such modal parameters from the measured response or its ...
Su‐Hong Kim +3 more
wiley +1 more source

