Atomate2: modular workflows for materials science

Alex M. Ganose; Hrushikesh Sahasrabuddhe; Mark Asta; Kevin Beck; Tathagata Biswas; Alexander Bonkowski; Joana Bustamante; Xin Chen; Yuan Chiang; Daryl C. Chrzan; Jacob Clary; Orion A. Cohen; Christina Ertural; Max C. Gallant; Janine George; Sophie Gerits; Rhys E. A. Goodall; Rishabh D. Guha; Geoffroy Hautier; Matthew Horton; T. J. Inizan; Aaron D. Kaplan; Ryan S. Kingsbury; Matthew C. Kuner; Bryant Li; Xavier Linn; Matthew J. McDermott; Rohith Srinivaas Mohanakrishnan; Aakash N. Naik; Jeffrey B. Neaton; Shehan M. Parmar; Kristin A. Persson; Guido Petretto; Thomas A. R. Purcell; Francesco Ricci; Benjamin Rich; Janosh Riebesell; Gian-Marco Rignanese; Andrew S. Rosen; Matthias Scheffler; Jonathan Schmidt; Jimmy-Xuan Shen; Andrei Sobolev; Ravishankar Sundararaman; Cooper Tezak; Victor Trinquet; Joel B. Varley; Derek Vigil-Fowler; Duo Wang; David Waroquiers; Mingjian Wen; Han Yang; Hui Zheng; Jiongzhi Zheng; Zhuoying Zhu; Anubhav Jain

doi:10.1039/D5DD00019J

Atomate2: modular workflows for materials science

Alex M. Ganose,

*^a Hrushikesh Sahasrabuddhe,

^bc Mark Asta,^bd Kevin Beck,^e Tathagata Biswas,

^f Alexander Bonkowski,^g Joana Bustamante,

^h Xin Chen,^bc Yuan Chiang,^bd Daryl C. Chrzan,^bd Jacob Clary,ⁱ Orion A. Cohen,^dq Christina Ertural,

^h Max C. Gallant,^bd Janine George,

^hk Sophie Gerits,^l Rhys E. A. Goodall,^m Rishabh D. Guha,

^d Geoffroy Hautier,ⁿ Matthew Horton,^o T. J. Inizan,^dqaf Aaron D. Kaplan,

^d Ryan S. Kingsbury,

^p Matthew C. Kuner,^bd Bryant Li,

^b Xavier Linn,^q Matthew J. McDermott,

^d Rohith Srinivaas Mohanakrishnan,^bd Aakash N. Naik,

^hk Jeffrey B. Neaton,^drs Shehan M. Parmar,

^ae Kristin A. Persson,

^bd Guido Petretto,^ft Thomas A. R. Purcell,

^uv Francesco Ricci,

^dft Benjamin Rich,

^w Janosh Riebesell,

^dmx Gian-Marco Rignanese,

^fty Andrew S. Rosen,

^z Matthias Scheffler,^v Jonathan Schmidt,

^j Jimmy-Xuan Shen,^aa Andrei Sobolev,^vab Ravishankar Sundararaman,

^ac Cooper Tezak,^l Victor Trinquet,

^f Joel B. Varley,^aa Derek Vigil-Fowler,ⁱ Duo Wang,^c David Waroquiers,^ft Mingjian Wen,

^ad Han Yang,^o Hui Zheng,^d Jiongzhi Zheng,

ⁿ Zhuoying Zhu^c and Anubhav Jain*^c

Author affiliations

* Corresponding authors

^a Department of Chemistry, Imperial College London, London W12 0BZ, UK
E-mail: a.ganose@imperial.ac.uk

^b Department of Materials Science and Engineering, University of California, Berkeley, California, USA

^c Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
E-mail: ajain@lbl.gov

^d Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

^e Program in Applied Mathematics, University of Arizona, 617 N. Santa Rita, Tucson, Arizona 85721, USA

^f UCLouvain, Institut de la Matière Condensée et des Nanosciences, Université catholique de Louvain, Chemin des Étoiles 8, Louvain-la-Neuve 1348, Belgium

^g Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany

^h Department Materials Chemistry, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany

ⁱ National Renewable Energy Laboratory, Golden, Colorado 80401, USA

^j Department of Materials, ETH Zürich, Zürich, Switzerland

^k Institute of Condensed Matter Theory and Solid-State Optics, Friedrich Schiller University Jena, Jena, Germany

^l Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80302, USA

^m Radical AI, Inc., New York City, NY, USA

ⁿ Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

^o Microsoft Research AI for Science, USA

^p Department of Civil and Environmental Engineering and the Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA

^q College of Chemistry, University of California, Berkeley, CA 94720, USA

^r Department of Physics, University of California, Berkeley, CA 94720, USA

^s Kavli Energy NanoSciences Institute at Berkeley, Berkeley, CA, USA

^t Matgenix SRL, A6K Advanced Engineering Centre, Square des Martyrs 1, 6000 Charleroi, Belgium

^u Department of Chemistry and Biochemistry, University of Arizona, 1306 East University Boulevard, Tucson, Arizona 85721, USA

^v The NOMAD Laboratory, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4–6, D-14195 Berlin, Germany

^w Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80302, USA

^x Cavendish Laboratory, University of Cambridge, J. J. Thomson Ave, Cambridge, UK

^y WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium

^z Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

^aa Lawrence Livermore National Laboratory, Livermore, CA, USA

^ab Molecular Simulations from First Principles – MS1P e.V., Clayallee 167, 14195 Berlin, Germany

^ac Department of Materials Science of Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA

^ad Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China

^ae School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA

^af Bakar Institute of Digital Materials for the Planet, University of California, Berkeley, CA 94720, USA

Abstract

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.

This article is part of the themed collection: 2023 and 2024 Accelerate Conferences

Digital Discovery

Atomate2: modular workflows for materials science

Abstract

Article information

Download Citation

Permissions

Atomate2: modular workflows for materials science

Social activity

Search articles by author

Spotlight

Advertisements