Autoencoder

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autoencoder.tex (29 lines)

\documentclass[tikz]{standalone}

\usepackage{neuralnetwork}

\newcommand{\xin}[2]{$x_#2$}
\newcommand{\xout}[2]{$\hat x_#2$}

\begin{document}
\begin{neuralnetwork}[height=8]
  \tikzstyle{input neuron}=[neuron, fill=orange!70];
  \tikzstyle{output neuron}=[neuron, fill=blue!60!black, text=white];

  \inputlayer[count=8, bias=false, title=Input Layer, text=\xin]

  \hiddenlayer[count=5, bias=false]
  \linklayers

  \hiddenlayer[count=3, bias=false, title=Latent\\Representation]
  \linklayers

  \hiddenlayer[count=5, bias=false]
  \linklayers

  \outputlayer[count=8, title=Output Layer, text=\xout]
  \linklayers

\end{neuralnetwork}
\end{document}

zT vs n

physicssolid state physicsthermodynamics

Thermoelectric figure of merit $zT$ vs carrier concentration $n$ for Bi2Te3 based on empirical data in $\alpha - \ln \sigma$ plot as a thermoelectric material performance indicator. Tuning $n$ for optimal $zT$ involves a compromise between thermal conductivity $\kappa$ , Seebeck coefficient $S$ and electrical conductivity $\sigma$ . Increasing the electrical conductivity $\sigma$ not only produces an increase in the electronic thermal conductivity $\kappa_\text{el}$ but also usually decreases the Seebeck coefficient $S$ . This makes optimal $zT$ difficult to achieve. Plot scales are $\kappa [W / m K] \in [0,10]$ , $S [mV] \in [0,500]$ , $\sigma [1/(\Omega cm)] \in [0,5000]$ .

Aviary

machine learningneural networksmaterials informatics

Aviary logo: a repo of machine learning models for representation learning in materials discovery from stoichiometry and symmetries via graph convolutions. For https://github.com/CompRhys/aviary.

Autoencoder

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autoencoder.tex (29 lines)