Results 61 to 70 of about 289,279 (277)

Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization

Advanced Functional Materials, EarlyView.
Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan, Zhiqiang Zhao, Oliver J. Conquest, Ying Shu, Haoyue Sun, Chunjing Su, Qi Cheng, Aloysius Soon, Catherine Stampfl, Jun Huang   +9 more
wiley   +1 more source

Data-efficient optimization of thermally-activated polymer actuators through machine learning

Materials & Design
For applications in soft robotics and smart textiles, thermally-activated, twisted, and coiled polymer actuators can offer high mechanical actuation with proper optimization of their processing conditions. However, optimization is often aggravated by the
Yuhao Zhang, Maija Vaara, Azin Alesafar, Duc Bach Nguyen, Pedro Silva, Laura Koskelo, Jussi Ristolainen, Matthias Stosiek, Joakim Löfgren, Jaana Vapaavuori, Patrick Rinke   +10 more
doaj   +1 more source

Extrinsic Bayesian Optimization on Manifolds

Algorithms, 2023
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and utilizing the uncertainty in ...
Yihao Fang, Mu Niu, Pokman Cheung, Lizhen Lin   +3 more
doaj   +1 more source

Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces [PDF]

, 2014
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures with an unknown number of layers. To relate performance data gathered for
Duvenaud, David, Hutter, Frank, Osborne, Michael A., Snoek, Jasper, Swersky, Kevin   +4 more
core  

Causal Bayesian Optimization

, 2020
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our
Aglietti, Virginia, Lu, Xiaoyu, Paleyes, Andrei, González, Javier   +3 more
openaire   +2 more sources

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Clean Up Behind You ‐ Novel Patterning Approach for Solid Immersion Lenses

Advanced Functional Materials, EarlyView.
A focused ion beam (FIB) milling strategy enables rapid fabrication of solid immersion lenses (SILs) with smooth, debris‐free surfaces eliminating the need for post‐processing. The optimized pattern improves efficiency and surface quality. SILs containing NV centers are also investigated, confirming the technique's suitability for quantum and photonic ...
Aleksei Tsarapkin, Stefan Facsko, Wentao Zhang, Anna Mogilatenko, Kilian Unterguggenberger, Bailiang Li, Cem Güney Torun, Gerhard Hobler, Tommaso Pregnolato, Tim Schröder, Katja Höflich   +10 more
wiley   +1 more source

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

Advanced 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, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni   +3 more
wiley   +1 more source

Multi-Objective BiLevel Optimization by Bayesian Optimization

Algorithms
In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who ...
Vedat Dogan, Steven Prestwich
doaj   +1 more source

Chiral scatterers designed by Bayesian optimization

, 2018
The helicity or chirality of scattered light is strongly linked to the dual symmetry of the scatterer. The latter depends on chiral materials or on scatterers which are not superimposable with their mirror image.
Burger, Sven, Gutsche, Philipp, Nieto-Vesperinas, Manuel, Schneider, Philipp-Immanuel   +3 more
core   +1 more source