scholarly journals Benchmark Datasets Incorporating Diverse Tasks, Sample Sizes, Material Systems, and Data Heterogeneity for Materials Informatics

Data in Brief ◽  
2021 ◽  
pp. 107262
Author(s):  
Ashley N. Henderson ◽  
Steven K. Kauwe ◽  
Taylor D. Sparks
Keyword(s):  
Materials Informatics ◽  
Sample Sizes ◽  
Material Systems ◽  
Benchmark Datasets ◽  
Data Heterogeneity

scholarly journals Compositionally-Restricted Attention-Based Network for Materials Property Prediction

2020 ◽  
Author(s):  
Anthony Wang ◽  
Steven Kauwe ◽  
Ryan Murdock ◽  
Taylor Sparks
Keyword(s):  
Best Practice ◽  
Attention Mechanism ◽  
Materials Informatics ◽  
Chemical Formula ◽  
Property Prediction ◽  
Keen Interest ◽  
Benchmark Datasets ◽  
Practice Methods

<div>In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials property predictions when only a chemical formula is provided.</div><div>Our results show that CrabNet's performance matches or exceeds current best practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet's architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by CrabNet's design.</div><div>We feel confident that CrabNet, and its attention-based framework, will be of keen interest to future materials informatics researchers. </div>

scholarly journals Compositionally-Restricted Attention-Based Network for Materials Property Prediction

2020 ◽  
Author(s):  
Anthony Wang ◽  
Steven Kauwe ◽  
Ryan Murdock ◽  
Taylor Sparks
Keyword(s):  
Best Practice ◽  
Attention Mechanism ◽  
Materials Informatics ◽  
Chemical Formula ◽  
Property Prediction ◽  
Keen Interest ◽  
Benchmark Datasets ◽  
Practice Methods

<div>In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials property predictions when only a chemical formula is provided.</div><div>Our results show that CrabNet's performance matches or exceeds current best practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet's architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by CrabNet's design.</div><div>We feel confident that CrabNet, and its attention-based framework, will be of keen interest to future materials informatics researchers. </div>

scholarly journals Compositionally restricted attention-based network for materials property predictions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Anthony Yu-Tung Wang ◽  
Steven K. Kauwe ◽  
Ryan J. Murdock ◽  
Taylor D. Sparks
Keyword(s):  
Best Practice ◽  
Materials Science ◽  
Attention Mechanism ◽  
Materials Informatics ◽  
Chemical Formula ◽  
Keen Interest ◽  
Benchmark Datasets ◽  
Practice Methods

AbstractIn this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that ’s performance matches or exceeds current best-practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how ’s architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by its design. We feel confident that and its attention-based framework will be of keen interest to future materials informatics researchers.

scholarly journals Compositionally-Restricted Attention-Based Network for Materials Property Prediction

2021 ◽  
Author(s):  
Anthony Wang ◽  
Steven Kauwe ◽  
Ryan Murdock ◽  
Taylor Sparks
Keyword(s):  
Best Practice ◽  
Attention Mechanism ◽  
Materials Informatics ◽  
Chemical Formula ◽  
Property Prediction ◽  
Keen Interest ◽  
Benchmark Datasets ◽  
Practice Methods

In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials property predictions when only a chemical formula is provided.Our results show that CrabNet's performance matches or exceeds current best practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet's architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by its design.We feel confident that CrabNet, and its attention-based framework, will be of keen interest to future materials informatics researchers. For trained model weights, please see: http://doi.org/10.5281/zenodo.4633866

Is Red Really Romantic?

2017 ◽  
Vol 48 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Gabrielle K. Lehmann ◽  
Robert J. Calin-Jageman
Keyword(s):  
Opposite Direction ◽  
Positive Control ◽  
Weak Effect ◽  
Sample Sizes ◽  
Quality Controls

Abstract. Red has been reported to enhance attraction for women rating men ( Elliot et al., 2010 ) and men rating women ( Elliot & Niesta, 2008 ). We replicated one of these studies online and in-person. To ensure rigor, we obtained original materials, planned for informative sample sizes, pre-registered our study, used a positive control, and adopted quality controls. For men, we found a very weak effect in the predicted direction (d = 0.09, 95% CI [−0.17, 0.34], N = 242). For women, we found a very weak effect in the opposite direction (d = −0.09, 95% CI [−0.30, 0.12], N = 360). The original studies may have overestimated the red effect, our studies may be an underestimate, or there could be strong moderation of the effect of red on attraction.

Norm-Block Sample Sizes: A Review of 15 Multidimensional Intelligence Tests

2013 ◽  
Author(s):  
Randy G. Floyd ◽  
Ryan L. Farmer ◽  
Sarah Irby ◽  
Phil Norfolk ◽  
Haley Hawkins ◽  
...  
Keyword(s):  
Intelligence Tests ◽  
Sample Sizes ◽  
Block Sample

On the Mathematical Basis of Zelen’s Prerandomized Designs

1985 ◽  
Vol 24 (03) ◽  
pp. 120-130 ◽  
Author(s):  
E. Brunner ◽  
N. Neumann
Keyword(s):  
Clinical Trial ◽  
Normal Distribution ◽  
Random Variables ◽  
Small Samples ◽  
Selection Effects ◽  
Sample Sizes ◽  
Mathematical Basis ◽  
Additional Assumptions

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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