Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session X61: Open Science and Open DataFocus Live Undergrad Friendly
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Sponsoring Units: GDS Chair: Maria Longobardi, Univ of Geneva |
Friday, March 19, 2021 8:00AM - 8:12AM Live |
X61.00001: FAIR and Reproducible High-Throughput Workflows with AiiDA Sebastiaan Huber, Nicola Marzari, Giovanni Pizzi The ever-growing availability of computational power and sustained development of computational methods have contributed much to recent scientific progress. This progress presents new challenges regarding the sheer amount of calculations and data to be managed. Next-generation exascale computing infrastructures will harden these challenges and require automated and scalable solutions. We have thus developed a comprehensive, robust, open source, high-throughput infrastructure AiiDA (http://aiida.net) dedicated to address the challenges in automated workflow management and data provenance storage. We discuss how AiiDA’s engine can now sustain throughputs of ~100'000 processes/hour, while automatically storing full data provenance graphs. These are stored in a database making data queryable, traversable, and directly enabling high-performance data analytics. Any simulation software can be interfaced to AiiDA via its open plugin repository. We demonstrate how AiiDA's workflow engine provides advanced automation and error handling features, allowing users to write modular workflows, interoperable between many different quantum or classical codes. We highlight how the resulting data can be disseminated on the Materials Cloud (http://materialscloud.org) in fully FAIR-compliant mode. |
Friday, March 19, 2021 8:12AM - 8:24AM Live |
X61.00002: Ontologies in Computational Materials Science Maja-Olivia Lenz-Himmer, Luca M. Ghiringhelli, Carsten Baldauf, Matthias Scheffler With the tremendous increase in the amount of data in materials science, new ways to store and annotate data are necessary to successfully implement the FAIR principles -- and to do good and new science efficiently. Consequently, ontologies have been of increasing interest as they serve as an advanced level of annotation enabling the semantic linking of data even across domains. The Novel-Materials Discovery (NOMAD) Repository is the largest database in materials science and provides a normalized, source-independent form of these data in the NOMAD Archive using the NOMAD Metainfo [1] as metadata schema. The NOMAD Metainfo includes a number of relations between concepts and therefore already goes beyond a metadata catalogue. We advanced it to an ontology and extended it to increase semantics based on the European Materials and Modeling Ontology (EMMO). Furthermore, within the NOMAD ecosystem, we created an ontology collection covering materials structures and properties and their semantic relations. We demonstrate how this enables connecting multiple sources of knowledge and semantic searches. The search for a better solar cell material is used as a first application example. |
Friday, March 19, 2021 8:24AM - 8:36AM Live |
X61.00003: The Organic Superconductor Database Owen Ganter, Charles C Agosta A database of low dimensional organic crystals containing structural information, electronic band structure calculations, and experimental measurements is presented. These materials exhibit a variety of phenomena including unconventional superconductivity, charge and spin density waves, and spin liquid states. The database is a versatile tool for scientists studying quantum materials and will pave the way towards new discoveries and materials, especially via machine learning methods. A website provides access to the database. The database contains an analysis of the geometric arrangement of donor molecules for each material. The band structure, density of states, and Fermi surface is available from calculations made by applying a tight binding model to charge transfer integrals obtained using Gaussian09. We will show how this data can be used to understand the relationship between crystal structure and band structure. We will also present a calculation relating the charge transfer integral phase space to the relative geometry between two donor molecules. We will discuss how the experimental measurements stored in the database can be analyzed in conjunction with band structure to probe the mechanism of correlated electron systems. |
Friday, March 19, 2021 8:36AM - 9:12AM Live |
X61.00004: The OpenAIRE Research Graph: Science as a public good Invited Speaker: Paolo Manghi The OpenAIRE Research Graph is one of the largest (if not the largest) collections of |
Friday, March 19, 2021 9:12AM - 9:24AM Live |
X61.00005: The NOMAD Artificial-Intelligence Toolkit: Web-Based FAIR-Data-Driven Materials Science Luigi Sbailò, Matthias Scheffler, Luca Ghiringhelli The Novel-Materials Discovery (NOMAD) Laboratory created and maintains the Repository & Archive, the largest data store of computational materials data worldwide, which stores more than 100 million calculations. Here, we present the NOMAD Artificial-Intelligence (AI) Toolkit, a web-based infrastructure for the interactive analysis of the material-science Findable, Accessible, Interoperable, and Recyclable (FAIR) data stored in the NOMAD Archive. By using Jupyter notebooks running in a web-browser (no software to be installed on the user side), the NOMAD data can be accessed and data mining, machine learning, and other AI techniques can be applied to analyze them. This infrastructure brings the concept of reproducibility in materials science to the next level, by allowing researchers to share, besides the data contributing to their scientific publications, also all the analytics tools they have created, adapted, and applied for unveiling patterns in them and predicting properties of known, new, or even novel materials. |
Friday, March 19, 2021 9:24AM - 9:36AM Live |
X61.00006: Discovery of rare-earth-free magnetic materials through databases Masahiro Sakurai, Renhai Wang, Timothy Liao, Chao Zhang, Huaijun Sun, Yang Sun, Haidi Wang, Xin Zhao, Songyou Wang, Balamurugan Balasubramanian, Xiaoshan Xu, David J Sellmyer, Vladimir P Antropov, Jianhua Zhang, Cai-Zhuang Wang, Kai Ming Ho, James Chelikowsky We build an open-access database specialized for magnetic compounds as well as magnetic clusters [1]. Our focus is on rare-earth-free magnets. We illustrate data-intensive methods to facillitate the theoretical and experimental discoveries of new magnetic materials [1,2]. In particular, we use an adaptive genetic algorithm (AGA) to efficiently explore a broad range of compositional and structural space. We carry out high-throughput first-principles calculations for AGA-derived stable and metastable structures, yielding a large array of datasets about crystallography, thermodynamic stability, and magnetic properties. We demonstrate the utility of our datasets for computational screening, machine-learning modeling, and experimental fabrication. |
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