The materials experiment knowledge graph

Statt, Michael J.; Rohr, Brian A.; Guevarra, Dan; Breeden, Ja'Nya; Suram, Santosh K.; Gregoire, John M.

doi:10.1039/d3dd00067b

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…This type of tighter integration is already being demonstrated in predictive synthesis. 25–30 There is still a critical gap between simulation and experiment, largely due to differences in materials representation, 31 but automated labs that integrate theory and experiment enable the creation of new multimodal datasets and models that can aid in the construction of a large experimental knowledge graph 32 and ultimately improve our fundamental understanding of materials.…”

Section: Discussionmentioning

confidence: 99%

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Hung,

Yager,

Monteverde

et al. 2024

Digital Discovery

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Section: Discussionmentioning

confidence: 99%

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Hung,

Yager,

Monteverde

et al. 2024

Digital Discovery

Self Cite

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“…In materials science, several knowledge graphs have been developed to integrate and organize data from various sources, including scientific papers, databases, and ontologies, to support data-driven research and discovery. Examples include the Materials Experiment Knowledge Graph 22 , the Materials Platform for Data Science (MPDS) 23 , Propnet 24 , and the Open Organic Materials Database 25 , among others. These knowledge graphs are being used to advance materials science research, from developing new materials to optimizing existing ones, and are helping to pave the way for more efficient and effective data-driven discovery in the field.…”

Section: Background and Summarymentioning

confidence: 99%

MatKG: An autonomously generated knowledge graph in Material Science

Venugopal,

Olivetti

2024

Sci Data

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In this paper, we present MatKG, a knowledge graph in materials science that offers a repository of entities and relationships extracted from scientific literature. Using advanced natural language processing techniques, MatKG includes an array of entities, including materials, properties, applications, characterization and synthesis methods, descriptors, and symmetry phase labels. The graph is formulated based on statistical metrics, encompassing over 70,000 entities and 5.4 million unique triples. To enhance accessibility and utility, we have serialized MatKG in both CSV and RDF formats and made these, along with the code base, available to the research community. As the largest knowledge graph in materials science to date, MatKG provides structured organization of domain-specific data. Its deployment holds promise for various applications, including material discovery, recommendation systems, and advanced analytics.

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“…In the realm of data provenance, Mitchell et al 29 proposed a data pipeline to support the modelling of the COVID pandemic, whereas ref. 30 devised a knowledge graph to record experiment provenance in materials research. Although these studies provide insights into building a collaborative research environment, they are developed in isolation with customised data interfaces.…”

Section: Introductionmentioning

confidence: 99%

A dynamic knowledge graph approach to distributed self-driving laboratories

Bai,

Mosbach,

Taylor

et al. 2024

Nat Commun

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The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist’s research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

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The materials experiment knowledge graph

Abstract: Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its...

Cited by 7 publications

References 42 publications

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

MatKG: An autonomously generated knowledge graph in Material Science

A dynamic knowledge graph approach to distributed self-driving laboratories

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