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.

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions.

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.

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

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 tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.

This is a file containing aggregation data for two proteins that were thermomechanically aggregated. The aggregated proteins were separated by asymmetric flow field flow fractionation and separated protein fractions were detected and quantified by UV spectrophotometry and multi-angle light scattering. The UV spectrophotometry was used to quantify the amount of residual monomer, which is reported herein. The multi-angle light scattering was fitted to a relevant model to calculate the molecular weight of the aggregated protein, also reported herein.