Code and demonstration data for the paper, "On-the-fly closed-loop materials discovery via Bayesian active learning," Kusne, A.G., Yu, H., Wu, C. et al. Nat Commun 11, 5966 (2020). https://doi.org/10.1038/s41467-020 19597-w Code: Closed-loop autonomous materials exploration and optimization. This code is used to control an autonomous materials exploration and optimization platform. It guides subsequent experiments to learn about a material's phase map and target functional properties in a unified framework.

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.).

This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.

This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.

SRM 3235 Soy Milk - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining vitamins, elements, proximates, fatty acids, and amino acids in soy milk and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a soy milk prepared by a commercial manufacturer. A unit of SRM 3235 consists of 10 ampoules, each containing approximately 10 mL of material. This data is public in the Certificate of Analysis for this material.

SRM 3239 Isoflavones Calibration Solution - This Standard Reference Material (SRM) is intended primarily for use in calibration of instruments and techniques used for the determination of isoflavones. SRM 3239 consists of two solutions containing isoflavones at levels that reflect the mass fraction ratios found in soy products. This data is public in the Certificate of Analysis for this material.

SRM 3252 Protein Drink Mix - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining proximates, fatty acids, cholesterol, vitamins, elements, and amino acids in protein drink mixes and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. This data is public in the Certificate of Analysis for this material.

SRM 3262 St. John's Wort (Hypericum perforatum L.) Aerial Parts - This Standard Reference Material (SRM) is intended primarily for use in validating analytical methods for the determination of chlorogenic acid, flavonoids, naphthodianthrones, and toxic elements in Hypericum perforatum L. and similar materials. This data is public in the Certificate of Analysis for this material.