Effect of P-Containing Ligands on the Structural and Optical Properties of (CdSe)n (n = 3, 6, 10) Clusters

Yu, Shengping; Huang, De-Lin; Yang, Mingli; Yang, Minghui

doi:10.1007/s10876-017-1186-0

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Optical properties are not only influenced by the number of ligands coordinated to the cluster but also by the specific ligand type. 108,109 The ligand induced blue-shift of the optical absorption of small CdSe clusters was also reported for other ligand types such as phosphines, 109,110 amines, 109,111 pyridine, 109 phosphine oxides, 109 and thiols. 112 The shape of the optical absorption spectrum is also strongly influenced by ligand passivation.…”

Section: G Comparison Of (Cd 2 Se 2 ) Q (Q = −1 0 +1) With Larger mentioning

confidence: 57%

Chemical bonding in initial building blocks of semiconductors: Geometrical structures and optical absorption spectra of isolated CdSe2+ and Cd2Se2+ species

Jäger

Shayeghi

Klippenstein

et al. 2018

The Journal of Chemical Physics

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Section: G Comparison Of (Cd 2 Se 2 ) Q (Q = −1 0 +1) With Larger mentioning

confidence: 57%

Chemical bonding in initial building blocks of semiconductors: Geometrical structures and optical absorption spectra of isolated CdSe2+ and Cd2Se2+ species

Jäger

Shayeghi

Klippenstein

et al. 2018

The Journal of Chemical Physics

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“…Many works on resource-aware machine learning focus on resource-aware inference (Tann et al 2016;Yu et al 2018;Amir and Givargis 2018;Li et al 2021a). These techniques allow adapting the inference to dynamically changing availability of resources at run-time but are not applicable to training.…”

Section: Related Workmentioning

confidence: 99%

DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems

Rapp¹,

Khalili²,

Pfeiffer³

et al. 2021

Preprint

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We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources. We present an adaptive, resource-aware, on-device learning mechanism, DISTREAL, which is able to fully and efficiently utilize the available resources on devices in a distributed manner, increasing the convergence speed. This is achieved with a dropout mechanism that dynamically adjusts the computational complexity of training an NN by randomly dropping filters of convolutional layers of the model. Our main contribution is the introduction of a design space exploration (DSE) technique, which finds Pareto-optimal per-layer dropout vectors with respect to resource requirements and convergence speed of the training. Applying this technique, each device is able to dynamically select the dropout vector that fits its available resource without requiring any assistance from the server. We implement our solution in a federated learning (FL) system, where the availability of computational resources varies both between devices and over time, and show through extensive evaluation that we are able to significantly increase the convergence speed over the state of the art without compromising on the final accuracy.

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GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

Jäger

Johnston

2019

Nanoscale

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Effect of P-Containing Ligands on the Structural and Optical Properties of (CdSe)n (n = 3, 6, 10) Clusters

Cited by 5 publications

References 38 publications

Chemical bonding in initial building blocks of semiconductors: Geometrical structures and optical absorption spectra of isolated CdSe2+ and Cd2Se2+ species

Chemical bonding in initial building blocks of semiconductors: Geometrical structures and optical absorption spectra of isolated CdSe2+ and Cd2Se2+ species

DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems

GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

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