Looking through glass: Knowledge discovery from materials science literature using natural language processing

Venugopal, Vineeth; Sahoo, Sourav; Zaki, Mohd; Agarwal, Manish; Gosvami, Nitya Nand; Krishnan, N. M. Anoop

doi:10.1016/j.patter.2021.100290

Cited by 36 publications

(27 citation statements)

References 51 publications

(111 reference statements)

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“…Traditionally, data has been shared through plots and tables in manuscripts, with important context embedded in manuscript text. While recent efforts make it possible to extract information from these documents using natural language processing methods, [47][48][49][50] traditional publications are not an efficient way of transmitting data. Fortunately, a slow shi towards more open data sharing is underway.…”

Section: Community Scale Data Managementmentioning

confidence: 99%

The laboratory of Babel: highlighting community needs for integrated materials data management

Pelkie

Pozzo

2023

Digital Discovery

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Section: Community Scale Data Managementmentioning

confidence: 99%

The laboratory of Babel: highlighting community needs for integrated materials data management

Pelkie

Pozzo

2023

Digital Discovery

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“…For oxide glasses, this involves automated literature screening to mine model training and validation data from text, tables, and figures. [ 201 ] ML models, mostly based on neural networks, have been developed to predict mechanical properties such as hardness and elastic modulus; [ 202 ] however, improved predictions will require physical descriptors instead of simple glass composition data. The search for such descriptors can be guided by classification‐based ML.…”

Section: Emerging Glass Types With Optical Transparency and Tailored ...mentioning

confidence: 99%

Advancing the Mechanical Performance of Glasses: Perspectives and Challenges

et al. 2022

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in terms of deciphering structure-property relationships and the nonaffine nature of glass mechanical behavior, of computational and computer-assisted methods for accelerated materials discovery, and of physicochemical insight at glass formation and synthesis in unconventional types of materials. Despite this progress, significant questions remain as to the fundamental role of structural heterogeneity and its consequences for the predictability of mechanical properties underlying the many applications of glass. Today's challenge is to translate new understanding of the physics of disorder, glass chemistry, and surface mechanics into tools that enable future glass products with adapted elasticity, strength, and toughness. We will therefore consider fundamental advances in relation to emerging glass applications. While the aforementioned physical insights have mostly been obtained by computational simulation of model systems, and often through the examination of metallic glasses, we will here focus on glasses suitable for visible transparency, in particular silicate glasses, such as a representative of today's most prolific glass devices, ranging from ultrathin substrates to strong and visually transparent cover materials. We will further consider hybrid glasses and glass-like composite materials as emerging alternatives that may overcome the ubiquitous conflict between strength and toughness. [1] Glasses are materials that lack a crystalline microstructure and long-range atomic order. Instead, they feature heterogeneity and disorder on superstructural scales, which have profound consequences for their elastic response, material strength, fracture toughness, and the characteristics of dynamic fracture. These structure-property relations present a rich field of study in fundamental glass physics and are also becoming increasingly important in the design of modern materials with improved mechanical performance. A first step in this direction involves glass-like materials that retain optical transparency and the haptics of classical glass products, while overcoming the limitations of brittleness. Among these, novel types of oxide glasses, hybrid glasses, phase-separated glasses, and bioinspired glass-polymer composites hold significant promise. Such materials are designed from the bottom-up, building on structure-property relations, modeling of stresses and strains at relevant length scales, and machine learning predictions. Their fabrication requires a more scientifically driven approach to materials design and processing, building on the physics of structural disorder and its consequences for structural rearrangements, defect initiation, and dynamic fracture in response to mechanical load. In this article, a perspective is provided on this highly interdisciplinary field of research in terms of its most recent challenges and opportunities.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202109029.

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“…A.Analisis Kebutuhan Fungsional Sistem yang dibuat memiliki dasar fungsional yang dapat menggambarkan fungsi yang harus dikerjakan oleh sistem untuk memenuhi kebutuhan user secara rinci mengenai data dan informasi yang berkaitan dengan Staff. Fungsional dari sistem yang adalah sebagai berikut: [7] Fungsi-fungsi yang dapat dilakukan Customer antara lain : Gambar 2. Flow Diagram CV.…”

Section: Analisis Proses Sistemunclassified

Implementasi Chatbot Untuk Laporan Bug / Error Reporting Dengan Natural Languange Processing (Studi Kasus : CV. Solusi Prima)

Hendri

2021

processor

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Research at CV. Solusi Prima was done to help implement chatbot on bug report, in order to solve some problems in interaction with client. We use some research method such as observation, interview, and library research. From this research's result there are some problem with working system that is client and staff in terms of report bug, make report which is done separately and manually (via whatsapp). This matter make staff become confused between chat to see report bug and for staff to make report bug for their head manager, and for head manager it's cost a lot of time to recap all of his staff's works in patch bug. Especially if their customer and the staff keep report error numerously, can cause older report bugs not being recorded in system, or some report being recorded but not tidy enough, so the staff will get confused to input report bug one by one, because of that we use this chatbot system based on natural language processing method with messenger application (telegram).Aim to make it easier for staff and client in making bug reports, so no need for staff to make report manually without having to lose previously datas, other than that the data that is display base on realtime so whatever report that is display will automatically broadcasted by system to all company's staffs from the results of the testing, system worked is compatible with what we are planning from the start. With the use of chatbot that have been equipped with natural language processing, make user easier to do reporting without moving their hands from keyboard to mouse too often. All the informations in chatbot will be saved and can be used for doing data analythic, so making chatbot easier to use and making it easy for staff and client to report a bug.

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Looking through glass: Knowledge discovery from materials science literature using natural language processing

Cited by 36 publications

References 51 publications

The laboratory of Babel: highlighting community needs for integrated materials data management

The laboratory of Babel: highlighting community needs for integrated materials data management

Advancing the Mechanical Performance of Glasses: Perspectives and Challenges

Implementasi Chatbot Untuk Laporan Bug / Error Reporting Dengan Natural Languange Processing (Studi Kasus : CV. Solusi Prima)

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