SRM 2387 Peanut Butter - This Standard Reference Material (SRM) is intended primarily for use in validating methods for determining proximates, fatty acids, calories, vitamins, elements, amino acids, aflatoxins, and acrylamide in peanut butter and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a creamy peanut butter prepared by a manufacturer of peanut butter products. A unit of SRM 2387 consists of three jars of peanut butter containing 170 g each.

A comparison of the expanded uncertainty for certified values of iodine in food matrix CRMs. The CRMS were found by searching The European Virtual Institute for Speciation Analysis (EVISA) database using the terms "Iodine" and "Certified" in the Material category. The uncertainties are expanded uncertainties at approximately 95% confidence.

JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.

The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.

The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo and molecular dynamics methods. It is a software written in C++ and python which is made publicly available to aid in reproducibility. It is also provided as a service to the scientific community in which there are few , if any, Monte Carlo programs that support flat histogram methods and advanced sampling algorithms. This software is expected to be updated frequently with new methods.

Source of the Refl1d program for modeling and analyzing neutron and X-ray reflectometry data, with the addition of distributed roughness capability.

Polymer Reference Interaction Site Model (PRISM) theory describes the equilibrium spatial-correlations of liquid-like polymer systems including melts, blends, solutions, block copolymers, ionomers, liquid crystal forming polymers and nanocomposites. Using PRISM theory, one can calculate thermodynamic (e.g., second virial coefficients, Flory-Huggins interaction parameters, potentials of mean force) and structural (eg., pair correlation functions, structure factors) information for these macromolecular materials.

We present the development of pyLLE, a freely accessible Lugiato-Lefever equation solver programmed in Python and Julia and optimized for the simulation of microresonator frequency combs. Examples illustrating its operation and performance are presented.

Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a parametric model - analogous to a fitting function - Bayesian inference uses each measurement "data point" to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

SEDCORR is an open-source Python module designed to correct for the systematic energy deficits in atom probe mass spectra of electrically insulating samples. The assumption of the algorithm is that the mass spectrum for a dataset is conserved throughout the dataset and that any changes to the peak positions arise from an unknown slowly-fluctuating accelerating voltage. For computational speed, the unknown accelerating voltage is determined using a template matching FFT-based cross correlation method.

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.