Results 141 to 150 of about 7,835 (281)

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

Concise network models of memory dynamics reveal explainable patterns in path data. [PDF]

Sci Adv
Sahasrabuddhe R, Lambiotte R, Rosvall M.
europepmc   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

Real-Time Deep-Learning Image Reconstruction and Instrument Tracking in MR-Guided Biopsies. [PDF]

J Magn Reson Imaging
Noordman CR, Te Molder LPW, Maas MC, Overduin CG, Fütterer JJ, Huisman HJ.   +5 more
europepmc   +1 more source

Sequence of potentials interpolating between the U(5) and E(5) symmetries [PDF]

, 2004
Dennis Bonatsos, D. Lenis, N. Minkov, P. P. Raychev, P. A. Terziev   +4 more
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

TPpred-CMvL: prediction of multi-functional therapeutic peptide using contrast multi-view learning. [PDF]

BMC Biol
Yan K, Xiang K, Chen Z, Chen S, Lu S, Liu B, Wang Y.   +6 more
europepmc   +1 more source

REPRESENTING AND INTERPOLATING SEQUENCES FOR HARMONIC BERGMAN FUNCTIONS ON THE UPPER HALF-SPACES(Spaces of Analytic and Harmonic Functions and Operator Theory)

, 1996
Boo Rim Choe
openalex   +1 more source

Interpolating sequences for the Nevanlinna and Smirnov classes [PDF]

, 2002
Andreas Hartmann, Xavier Massaneda, Artur Nicolau   +2 more
openalex   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source