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    <issue>93</issue>
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    <title language="eng">Jobflow: Computational Workflows Made Simple</title>
    <abstract language="eng">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&#13;
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&#13;
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.</abstract>
    <parentTitle language="eng">Journal of Open Source Software</parentTitle>
    <identifier type="doi">10.21105/joss.05995</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-593104</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Andrew S. Rosen</author>
    <author>Max Gallant</author>
    <author>Janine George</author>
    <author>Janosh Riebesell</author>
    <author>Hrushikesh Sahasrabuddhe</author>
    <author>Jimmy-Xuan Shen</author>
    <author>Mingjian Wen</author>
    <author>Matthew L. Evans</author>
    <author>Guido Petretto</author>
    <author>David Waroquiers</author>
    <author>Gian-Marco Rignanese</author>
    <author>Kristin A. Persson</author>
    <author>Anubhav Jain</author>
    <author>Alex M. Ganose</author>
    <subject>
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      <type>uncontrolled</type>
      <value>Automation</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Workflow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computational Materials Science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computations</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software</value>
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    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
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    <edition/>
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    <publisherName>American Chemical Society (ACS)</publisherName>
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    <title language="eng">Atomate2: Modular workflows for materials science</title>
    <abstract language="eng">High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.</abstract>
    <parentTitle language="eng">ChemRxiv</parentTitle>
    <identifier type="doi">10.26434/chemrxiv-2025-tcr5h</identifier>
    <identifier type="urn">urn:nbn:de:kobv:b43-624487</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alex Ganose</author>
    <author>Hrushikesh Sahasrabuddhe</author>
    <author>Mark Asta</author>
    <author>Kevin Beck</author>
    <author>Tathagata Biswas</author>
    <author>Alexander Bonkowski</author>
    <author>Joana Bustamante</author>
    <author>Xin Chen</author>
    <author>Yuan Chiang</author>
    <author>Daryl Chrzan</author>
    <author>Jacob Clary</author>
    <author>Orion Cohen</author>
    <author>Christina Ertural</author>
    <author>Janine George</author>
    <author>Max Gallant</author>
    <author>Janine George</author>
    <author>Sophie Gerits</author>
    <author>Rhys Goodall</author>
    <author>Rishabh Guha</author>
    <author>Geoffroy Hautier</author>
    <author>Matthew Horton</author>
    <author>Aaron Kaplan</author>
    <author>Ryan Kingsbury</author>
    <author>Matthew Kuner</author>
    <author>Bryant Li</author>
    <author>Xavier Linn</author>
    <author>Matthew McDermott</author>
    <author> Rohith Srinivaas Mohanakrishnan</author>
    <author>Aakash Naik</author>
    <author>Jeffrey Neaton</author>
    <author>Kristin Persson</author>
    <author>Guido Petretto</author>
    <author>Thomas Purcell</author>
    <author>Francesco Ricci</author>
    <author>Benjamin Rich</author>
    <author>Janosh Riebesell</author>
    <author>Gian-Marco Rignanese</author>
    <author>Andrew Rosen</author>
    <author>Matthias Scheffler</author>
    <author>Jonathan Schmidt</author>
    <author>Jimmy-Xuan Shen</author>
    <author>Andrei Sobolev</author>
    <author>Ravishankar Sundararaman</author>
    <author>Cooper Tezak</author>
    <author>Victor Trinquet</author>
    <author>Joel Varley</author>
    <author>Derek Vigil-Fowler</author>
    <author>Duo Wang</author>
    <author>David Waroquiers</author>
    <author>Mingjian Wen</author>
    <author>Han Yang</author>
    <author>Hui Zheng</author>
    <author>Jiongzhi Zheng</author>
    <author>Zhuoying Zhu</author>
    <author>Anubhav Jain</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DFT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digitalisation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learned interatomic potentials</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
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    <collection role="unnumberedseries" number="">Preprints der BAM</collection>
    <thesisPublisher>Bundesanstalt für Materialforschung und -prüfung (BAM)</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-bam/files/62448/atomate2-modular-workflows-for-materials-science.pdf</file>
  </doc>
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    <id>63616</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
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    <pageLast>11</pageLast>
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    <title language="eng">Accelerated data-driven materials science with the Materials Project</title>
    <abstract language="eng">The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world.&#13;
This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational Methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our eforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.</abstract>
    <parentTitle language="eng">Nature Materials</parentTitle>
    <identifier type="issn">1476-1122</identifier>
    <identifier type="doi">10.1038/s41563-025-02272-0</identifier>
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    <title language="eng">Atomate2: Modular workflows for materials science</title>
    <abstract language="eng">High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.</abstract>
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    <abstract language="eng">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.&#13;
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    <title language="eng">Correction: Atomate2: Modular workflows for materials science</title>
    <abstract language="eng">Correction for “Atomate2: modular workflows for materials science” by Alex M. Ganose et al., Digital Discovery, 2025, 4, 1944–1973, https://doi.org/10.1039/D5DD00019J.</abstract>
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