Materials Science vs. Data Science

AbstractIn addition to student assessment, curriculum assessment is a critical element to any pedagogy. It helps the educator assess the teaching of concepts, determine what may be lacking, and make changes for continual improvement. Meaningful assessment can be complicated when disciplines converge or when new approaches are implemented. To facilitate this, we present a network-based visualization schema to represent a materials informatics curriculum that combines materials science and data science concepts. We analyze the curriculum using network representations and relevant concepts from graph theory. This reveals established connections, linkages between materials science and data science, and the extent to which different concepts are connected. We also describe how some materials science topics are introduced from a data perspective, and present an illustrative case study from the curriculum.

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Methods for Open and Reproducible Materials Science

10.31235/osf.io/ag8zu ◽

2019 ◽

Author(s):

Sara L Wilson ◽

Micah Altman ◽

Rafael Jaramillo

Keyword(s):

Data Management ◽

Data Science ◽

Management Practices ◽

Materials Science ◽

Critical Role ◽

Structured Interview ◽

Open Science ◽

Manuscript Submission ◽

Attitudes And Practices ◽

Data Stewardship

Data stewardship in experimental materials science is increasingly complex and important. Progress in data science and inverse-design of materials give reason for optimism that advances can be made if appropriate data resources are made available. Data stewardship also plays a critical role in maintaining broad support for research in the face of well-publicized replication failures (in different fields) and frequently changing attitudes, norms, and sponsor requirements for open science. The present-day data management practices and attitudes in materials science are not well understood. In this article, we collect information on the practices of a selection of materials scientists at two leading universities, using a semi-structured interview instrument. An analysis of these interviews reveals that although data management is universally seen as important, data management practices vary widely. Based on this analysis, we conjecture that broad adoption of basic file-level data sharing at the time of manuscript submission would benefit the field without imposing substantial burdens on researchers. More comprehensive solutions for lifecycle open research in materials science will have to overcome substantial differences in attitudes and practices.

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Intellectual Community as a Bridge of Interdisciplinary Graduate Education in Materials Data Science

MRS Advances ◽

10.1557/adv.2020.140 ◽

2020 ◽

Vol 5 (7) ◽

pp. 355-362

Author(s):

Chi-Ning Chang ◽

Clinton A. Patterson ◽

Willie C. Harmon ◽

Debra A. Fowler ◽

Raymundo Arroyave

Keyword(s):

Data Science ◽

Graduate Program ◽

Materials Science ◽

Training Model ◽

Materials Informatics ◽

Materials Development ◽

Intellectual Community ◽

Interdisciplinary Learning ◽

Interdisciplinary Approaches ◽

Materials Genome

AbstractRecognizing materials development was advancing slower than technological needs, the 2011 the Materials Genome Initiative (MGI) advocated interdisciplinary approaches employing an informatics framework in materials discovery and development. In response, an interdisciplinary graduate program, funded by the National Science Foundation, was designed at the intersection of materials science, materials informatics, and engineering design, aiming to equip the next generation of scientists and engineers with Material Data Science. Based on the 4- year implementation experience, this report demonstrates how intellectual communities bridge students interdisciplinary learning processes and support a transition from disciplinary grounding to interdisciplinary learning and research. We hope this training model can benefit other interdisciplinary graduate programs, and produce a more productive and interdisciplinary materials workforce.

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Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

10.26434/chemrxiv-2022-d1pzz ◽

2022 ◽

Author(s):

Souvik Manna ◽

Diptendu Roy ◽

Sandeep Das ◽

Biswarup Pathak

Keyword(s):

Machine Learning ◽

Energy Storage ◽

Metal Ion ◽

Data Science ◽

Electrode Materials ◽

Performance Metrics ◽

Materials Science ◽

Specific Capacity ◽

High Energy ◽

Percentage Error

Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important parameters for choosing suitable electrode materials for high energy storage metal ion battery. Exploration of suitable electrode materials for metal ion batteries other than Li ion batteries (LIBs) has been deficient, though there is a need to develop alternative battery technologies with higher energy storage characteristics and environmental safety. In this work, we have considered Li, Na and K-ion electrode materials and their available battery data from Materials Project database to predict specific capacity of prospective K-ion battery electrode materials. Suitable features have been considered and developed to train the various ML algorithms. Mean Absolute Percentage Error has been considered as the performance metrics for assessment of different ML algorithms and among them, kernel ridge regression has been adopted as the most useful to predict the capacity of unknown electrode materials for K-ion battery. Using the value of specific capacity, the number of intercalated K ions in the formula unit of the non-intercalated electrode material compounds have also been calculated. DFT calculations have also been performed to verify the results obtained through ML. Our result shows ML is an encouraging alternative to computationally demanding DFT process as it can screen electrode materials rapidly for battery.

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Chances and Challenges in Fusing Data Science with Materials Science

Practical Metallography ◽

10.3139/147.110539 ◽

2018 ◽

Vol 55 (8) ◽

pp. 493-514 ◽

Cited By ~ 1

Author(s):

A. Prakash ◽

S. Sandfeld

Keyword(s):

Data Science ◽

Materials Science

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Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

MRS Advances ◽

10.1557/adv.2020.171 ◽

2020 ◽

Vol 5 (7) ◽

pp. 329-346 ◽

Cited By ~ 1

Author(s):

Thomas J. Oweida ◽

Akhlak Mahmood ◽

Matthew D. Manning ◽

Sergei Rigin ◽

Yaroslava G. Yingling

Keyword(s):

Data Science ◽

Materials Science ◽

Optimization Techniques ◽

Materials Informatics ◽

Science And Engineering ◽

Educational Challenges ◽

Engineering Problems ◽

Materials Science And Engineering ◽

Materials Genome ◽

Materials Genome Initiative

ABSTRACTSince the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects.

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Machine Learning for Structural Materials

Annual Review of Materials Research ◽

10.1146/annurev-matsci-110519-094700 ◽

2020 ◽

Vol 50 (1) ◽

pp. 27-48 ◽

Cited By ~ 2

Author(s):

Taylor D. Sparks ◽

Steven K. Kauwe ◽

Marcus E. Parry ◽

Aria Mansouri Tehrani ◽

Jakoah Brgoch

Keyword(s):

Machine Learning ◽

Data Science ◽

Mechanical Response ◽

Materials Science ◽

Multiple Scales ◽

Dimensional Space ◽

Structural Materials ◽

High Entropy ◽

Composition Property ◽

Using Data

The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials.

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Materials informatics and sustainability—The case for urgency

Data-Centric Engineering ◽

10.1017/dce.2021.19 ◽

2021 ◽

Vol 2 ◽

Author(s):

Hannah R. Melia ◽

Eric S. Muckley ◽

James E. Saal

Keyword(s):

Data Science ◽

Materials Science ◽

Advanced Materials ◽

Materials Informatics ◽

Design Development ◽

Cultural Resistance ◽

Scientific Methods ◽

Skill Gaps ◽

Digital Twins ◽

Global Environmental

Abstract The development of transformative technologies for mitigating our global environmental and technological challenges will require significant innovation in the design, development, and manufacturing of advanced materials and chemicals. To achieve this innovation faster than what is possible by traditional human intuition-guided scientific methods, we must transition to a materials informatics-centered paradigm, in which synergies between data science, materials science, and artificial intelligence are leveraged to enable transformative, data-driven discoveries faster than ever before through the use of predictive models and digital twins. While materials informatics is experiencing rapidly increasing use across the materials and chemicals industries, broad adoption is hindered by barriers such as skill gaps, cultural resistance, and data sparsity. We discuss the importance of materials informatics for accelerating technological innovation, describe current barriers and examples of good practices, and offer suggestions for how researchers, funding agencies, and educational institutions can help accelerate the adoption of urgently needed informatics-based toolsets for science in the 21st century.

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Data science applications for predictive maintenance and materials science in context to Industry 4.0

Materials Today Proceedings ◽

10.1016/j.matpr.2021.01.357 ◽

2021 ◽

Cited By ~ 2

Author(s):

Sufiyan Sajid ◽

Abid Haleem ◽

Shashi Bahl ◽

Mohd Javaid ◽

Tarun Goyal ◽

...

Keyword(s):

Industry 4.0 ◽

Data Science ◽

Materials Science ◽

Predictive Maintenance

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Trends in Electron Energy Loss Spectroscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078675 ◽

1977 ◽

Vol 35 ◽

pp. 228-229

Author(s):

C. Colliex ◽

P. Trebbia

Keyword(s):

Energy Loss ◽

Electron Energy ◽

Electron Energy Loss Spectroscopy ◽

Materials Science ◽

Analytical Technique ◽

Recent Developments ◽

Quantitative Results ◽

Electron Microscopes ◽

Electron Energy Loss ◽

Primary Electrons

The physical foundations for the use of electron energy loss spectroscopy towards analytical purposes, seem now rather well established and have been extensively discussed through recent publications. In this brief review we intend only to mention most recent developments in this field, which became available to our knowledge. We derive also some lines of discussion to define more clearly the limits of this analytical technique in materials science problems.The spectral information carried in both low ( 0<ΔE<100eV ) and high ( >100eV ) energy regions of the loss spectrum, is capable to provide quantitative results. Spectrometers have therefore been designed to work with all kinds of electron microscopes and to cover large energy ranges for the detection of inelastically scattered electrons (for instance the L-edge of molybdenum at 2500eV has been measured by van Zuylen with primary electrons of 80 kV). It is rather easy to fix a post-specimen magnetic optics on a STEM, but Crewe has recently underlined that great care should be devoted to optimize the collecting power and the energy resolution of the whole system.

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