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We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
The overall interest in nanotoxicity, triggered by the increasing use of nanomaterials in the material and life sciences, and the synthesis of an ever increasing number of new functional nanoparticles calls for standardized test procedures1,2 and for efficient approaches to screen the potential genotoxicity of these materials. Aiming at the development of fast and easy to use, automated microscopic methods for the determination of the genotoxicity of different types of nanoparticles, we assess the potential of the fluorometric γH2AX assay for this purpose. This assay, which can be run on an automated microscopic detection system, relies on the detection of DNA double strand breaks as a sign for genotoxicity3. Here, we provide first results obtained with broadly used nanomaterials like CdSe/CdS and InP/ZnS quantum dots as well as iron oxide, gold, and polymer particles of different surface chemistry with previously tested colloidal stability and different cell lines like Hep-2 and 8E11 cells, which reveal a dependence of the genotoxicity on the chemical composition as well as the surface chemistry of these nanomaterials. These studies will be also used to establish nanomaterials as positive and negative genotoxicity controls or standards for assay performance validation for users of this fluorometric genotoxicity assay. In the future, after proper validation, this microscopic platform technology will be expanded to other typical toxicity assays.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.
The Meticulous Approach: Fully traceable X-ray scattering data via a comprehensive lab methodology
(2021)
To find out if experimental findings are real, you need to be able to repeat them. For a long time, however, papers and datasets could not necessarily include sufficient details to accurately repeat experiments, leading to a reproducibility crisis. It is here, that the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration) tries to implement change – at least for small- and wide-angle X-ray scattering (SAXS/WAXS).
In the MOUSE project, we have combined: a) a comprehensive laboratory workflow with b) a heavily modified, highly automated Xenocs Xeuss 2.0 instrumental component. This combination allows us to collect fully traceable scattering data, with a well-documented data flow (akin to what is found at the more automated beamlines). With two full-time researchers, the lab collects and interprets thousands of datasets, on hundreds of samples for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
While these numbers do not light a candle to those achieved by our hardworking compatriots at the synchrotron beamlines, the laboratory approach does allow us to continually modify and fine-tune the integral methodology. So for the last three years, we have incorporated e.g. FAIR principles, traceability, automated processing, data curation strategies, as well as a host of good scattering practices into the MOUSE system. We have concomitantly expanded our purview as specialists to include an increased responsibility for the entire scattering aspect of the resultant publications, to ensure full exploitation of the data quality, whilst avoiding common pitfalls.
This talk will discuss the MOUSE project1 as implemented to date, and will introduce foreseeable upgrades and changes. These upgrades include better pre-experiment sample scattering predictions to filter projects on the basis of their suitability, exploitation of the measurement database for detecting long-term changes and automated flagging of datasets, and enhancing MC fitting with sample scattering simulations for better matching of odd-shaped scatterers.
Measuring an X-ray scattering pattern is relatively easy, but measuring a steady stream of high-quality, useful patterns requires significant effort and good laboratory organization.
Such laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration).
With the MOUSE, we have combined a comprehensive and highly automated laboratory workflow with a heavily modified X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, within a well-documented, FAIR-compliant data flow (akin to what is found at the more automated synchrotron beamlines). With two full-time researchers, our lab collects and interprets thousands of datasets, on hundreds of samples, for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
This talk will briefly introduce the foundations of X-ray scattering, present the MOUSE project, and will highlight the proven utility of the methodology for materials science. Upgrades to the methodology will also be discussed, as well as possible avenues for transferring this holistic methodology to other instruments
By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
McSAS3
(2023)
McSAS3 is a refactored version of the original McSAS (see DOI 10.1107/S1600576715007347). This software fits scattering patterns to obtain size distributions without assumptions on the size distribution form. The refactored version has some neat features:
- Multiprocessing is included, spread out over as many cores as number of repetitions!
- Full state of the optimization is stored in an organized HDF5 state file.
- Histogramming is separate from optimization and a result can be re-histogrammed as many times as desired.
- SasModels allow a wide range of models to be used
- If SasModels does not work (e.g. because of gcc compiler issues on Windows or Mac), an internal sphere model is supplied
- Simulated data of the scattering of a special shape can also be used as a McSAS fitting model. Your models are infinite!
- 2D fitting also works.
While the synthesis of Metal-Organic Framework (MOF) particles can be as easy as adding two solutions together, reproducibly obtaining the same particles, time and time again, is a lot harder. As laboratory-independent reproducibility is a cornerstone of the scientific method, we must put effort into finding and controlling all necessary parameters to achieve this.
An open-source Python/EPICS-controlled robotic platform (see picture) was adapted to systematically explore this for a 20 ml MOF synthesis of the Zeolitic Imidazole Framework-8 (ZIF-8) chemistry in methanol. Parameters that were explored included: 1) addition sequence, 2) addition speeds, 3) reaction times, 4) source chemicals, 5) stirring speeds, 6) stirring bar choice, 7) starting concentrations, and 8) workup methodologies. It was found that, by controlling these parameters, highly reproducible syntheses are obtained. Secondly, the variation of these parameters alone led to a dramatic difference in volume-weighted particle size means, which exceeds an order of magnitude as investigated by our in-house X-ray scattering instrument [1].
The syntheses are thoroughly documented in an automated fashion, and the synthesis libraries as well as analyses libraries will become available in batches soon. With this library, it will be possible to extract previously unknown correlations, and other laboratories can produce specific particles by following the exact procedures of the particles of their choice.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.