OPTIMADE, an API for exchanging materials data

Andersen, Casper Welzel; Armiento, Rickard; Blokhin, Evgeny; Conduit, Gareth; Dwaraknath, Shyam; Evans, Matthew L.; Fekete, Ádám; Gopakumar, Abhijith; Gražulis, S.; Merkys, Andrius; Mohamed, Fawzi; Oses, Corey; Pizzi, Giovanni; Rignanese, Gian‐Marco; Scheidgen, Markus; Talirz, Leopold; Toher, Cormac; Winston, Donald; Aversa, Rossella; Choudhary, Kamal; Colinet, Pauline; Curtarolo, Stefano; Stefano, Davide Di; Draxl, Claudia; Er, Süleyman; Esters, Marco; Fornari, Marco; Giantomassi, Matteo; Govoni, Marco; Hautier, Geoffroy; Hegde, V.; Horton, Matthew K.; Huck, P.; Huhs, Georg; Hummelshøj, Jens Strabo; Kariryaa, Ankit; Kozinsky, Boris; Kumbhar, Snehal; Liu, Mohan; Marzari, Nicola; Morris, Andrew J.; Mostofi, Arash A.; Persson, Kristin A.; Petretto, Guido; Purcell, Thomas A. R.; Ricci, Francesco; Rose, Frisco; Scheffler, Matthias; Speckhard, Daniel T.; Uhrin, Martin; Vaitkus, Antanas; Villars, P.; Waroquiers, David; Wolverton, Chris; Wu, Michael; Yang, Xiaoyu

doi:10.1038/s41597-021-00974-z

Cited by 56 publications

(39 citation statements)

References 32 publications

(38 reference statements)

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“…The OPTIMADE API specification (Andersen et al, 2020(Andersen et al, , 2021, released in July 2020, aimed to reduce these costs by designing a common API for use across a consortium of collaborating materials databases and beyond. Whilst based on the robust JSON:API standard (Katz et al, 2015), the OPTIMADE API specification presents several domain-specific features and requirements that can be tricky to implement for non-specialist teams.…”

Section: Discussionmentioning

confidence: 99%

“…The Python classes within the optimade package have been extended to make use of AiiDA and its underlying PostgreSQL storage engine. • The optimade.adapters module from the optimade-python-tools library is used in a graphical web client hosted on Materials Cloud (Andersen, 2021). It allows users to query OPTIMADE API implementations using user-friendly widgets as well as raw filter strings.…”

Section: Use Casesmentioning

confidence: 99%

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optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Evans¹,

Andersen²,

Dwaraknath³

et al. 2021

JOSS

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Section: Discussionmentioning

confidence: 99%

Section: Use Casesmentioning

confidence: 99%

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Evans¹,

Andersen²,

Dwaraknath³

et al. 2021

JOSS

Self Cite

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“…Vast amounts of data of different origin may be utilized to understand and design catalytic processes [ 67 , 68 ]. A substantial number of publicly accessible databases [ 69 – 92 ] has become available along with software packages encoding general workflows to interact with these databases [ 93 – 104 ]. Screening these data can produce valuable property predictions [ 105 – 109 ].…”

Section: Computational Catalysis and Mechanism Explorationmentioning

confidence: 99%

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Steiner

Reiher

2022

Top Catal

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Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

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“…Such activities focused on publishing data in computerreadable formats are important 131,132 but lie beyond the scope of this review. We do note that many subdisciplines within materials science, such as thermochemistry 133 and atomic-scale modeling, 134 are establishing community standards needed to bring order to a larger proportion of materials engineering data.…”

Section: Publishing Structured Datamentioning

confidence: 99%

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Hong

Ward

Chard

et al. 2021

JOM

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Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

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OPTIMADE, an API for exchanging materials data

Cited by 56 publications

References 32 publications

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Challenges and Advances in Information Extraction from Scientific Literature: a Review

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