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Dagdelen, John

Publications and source records attributed to Dagdelen, John.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org/, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f 1 ) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex "meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. Finally, all of our data and functionality has been made freely available on our Github ( https://github.com/materialsintelligence/matscholar ) and website ( http://matscholar.com ), and we expect these results to accelerate the pace of future materials science discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Unsupervised word embeddings capture latent knowledge from materials science literature

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

We introduce atomate, an open-source Python framework for computational materials science simula-tion, analysis, and design with an emphasis on automation and extensibility. Built on top of open sourcePython packages already in use by the materials community such as pymatgen, FireWorks, and custodian,atomate provides well-tested workflow templates to compute various materials properties such as elec-tronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomatealso enables the computational characterization of materials by providing workflows that calculate X-rayabsorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate isthat it provides both fully functional workflows as well as reusable components that enable one to com-pose complex materials science workflows that use a diverse set of computational tools. Additionally,atomate creates output databases that organize the results from individual calculations and contains abuilder framework that creates summary reports for each computed material based on multiplesimulations.

Mathew, Kiran↗
DOE OSTI2017

Computational prediction of new auxetic materials

Auxetics comprise a rare family of materials that manifest negative Poisson’s ratio, which causes an expansion instead of contraction under tension. Most known homogeneously auxetic materials are porous foams or artificial macrostructures and there are few examples of inorganic materials that exhibit this behavior as polycrystalline solids. It is now possible to accelerate the discovery of materials with target properties, such as auxetics, using high-throughput computations, open databases, and efficient search algorithms. Candidates exhibiting features correlating with auxetic behavior were chosen from the set of more than 67 000 materials in the Materials Project database. Poisson’s ratios were derived from the calculated elastic tensor of each material in this reduced set of compounds. We report that this strategy results in the prediction of three previously unidentified homogeneously auxetic materials as well as a number of compounds with a near-zero homogeneous Poisson’s ratio, which are here denoted “anepirretic materials”.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Application of Sol-Gel Method as a Protective Layer on a Specular Reflective Surface for Secondary Reflector in a Solar Receiver

Highly-specular reflective surfaces that can withstand elevated-temperatures are desirable for many applications including reflective heat shielding in solar receivers and secondary reflectors, which can be used between primary concentrators and heat collectors. A high-efficiency, high-temperature solar receiver design based on arrays of cavities needs a highly-specular reflective surface on its front section to help sunlight penetrate into the absorber tubes for effective flux spreading. Since this application is for high-temperature solar receivers, this surface needs to be durable and to maintain its optical properties through the usable life. Degradation mechanisms associated with elevated temperatures and thermal cycling, which include cracking, delamination, corrosion/oxidation, and environmental effects, could cause the optical properties of surfaces to degrade rapidly in these conditions. Protected mirror surfaces for these applications have been tested by depositing a thin layer of SiO2 on top of electrodeposited silver by means of the sol-gel method. To obtain an effective thin film structure, this sol-gel procedure has been investigated extensively by varying process parameters that affect film porosity and thickness. Endurance tests have been performed in a furnace at 150 degrees C for thousands of hours. This paper presents the sol-gel process for intermediate-temperature specular reflective coatings and provides the long-term reliability test results of sol-gel protected silver-coated surfaces.

14 SOLAR ENERGY↗