Results 51 to 60 of about 96,348 (272)

Inefficiency of K-FAC for Large Batch Size Training

open access: yes, 2019
In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch.
Gholami, Amir   +6 more
core   +1 more source

Circular‐Polarization‐Sensitive Organic Photodetectors with a Chiral Nanopatterned Electrode Inverse‐Designed by Genetic Algorithm

open access: yesAdvanced Functional Materials, EarlyView.
A chiral photodetector capable of selectively distinguishing left‐ and right‐handed circularly polarized light is experimentally demonstrated. The device, which features a nanopatterned electrode inverse‐designed by a genetic algorithm within a metal–dielectric–metal nanocavity that incorporates a vacuum‐deposited small‐molecule multilayer, exhibits ...
Kyung Ryoul Park   +3 more
wiley   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

open access: yesAdvanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi   +2 more
wiley   +1 more source

Slice sampling covariance hyperparameters of latent Gaussian models [PDF]

open access: yes, 2010
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters.
Adams, Ryan Prescott, Murray, Iain
core   +5 more sources

Semantic variation operators for multidimensional genetic programming

open access: yes, 2019
Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to
Cava William La   +7 more
core   +1 more source

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

open access: yesAdvanced Functional Materials, EarlyView.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu   +3 more
wiley   +1 more source

Redefining Therapies for Drug‐Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design

open access: yesAdvanced Healthcare Materials, EarlyView.
Antimicrobial peptide (AMP)‐loaded nanocarriers provide a multifunctional strategy to combat drug‐resistant Mycobacterium tuberculosis. By enhancing intracellular delivery, bypassing efflux pumps, and disrupting bacterial membranes, this platform restores phagolysosome fusion and macrophage function.
Christian S. Carnero Canales   +11 more
wiley   +1 more source

Hyperparameter Tuning and Optimization Applications

open access: yes, 2023
AbstractThis chapter reflects on advantages and sense of use of Hyperparameter Tuning (HPT) and its disadvantages. In particular it shows how important it is, to keep the human in the loop, even if HPT works perfectly. The chapter presents a collection of HPT studies. First, HPT applications in Machine Learning (ML) and Deep Learning (DL) are described.
openaire   +1 more source

Unveiling the Role of Curvature in Carbon for Improved Energy Release of Ammonium Perchlorate

open access: yesAdvanced Materials, EarlyView.
High‐curvature carbon materials identified via machine learning and simulation can enhance the heat release and combustion performance of ammonium perchlorate. ABSTRACT The catalytic role of carbon curvature in the thermal decomposition of ammonium perchlorate (AP) remains largely unexplored. To address this gap, this study employs machine learning and
Dan Liu   +8 more
wiley   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

open access: yesAdvanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll   +19 more
wiley   +1 more source

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