scholarly journals Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

APL Materials ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 053208 ◽  
Author(s):  
Ankit Agrawal ◽  
Alok Choudhary
Keyword(s):  
Big Data ◽  
Materials Science ◽  
Materials Informatics ◽  
Data Realization

scholarly journals Enabling deeper learning on big data for materials informatics applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dipendra Jha ◽  
Vishu Gupta ◽  
Logan Ward ◽  
Zijiang Yang ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Materials Science ◽  
Prediction Models ◽  
Model Performance ◽  
Materials Informatics ◽  
Learning Framework ◽  
Significant Attention

AbstractThe application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

scholarly journals NOMAD: The FAIR concept for big data-driven materials science

MRS Bulletin ◽  
2018 ◽  
Vol 43 (9) ◽  
pp. 676-682 ◽  
Author(s):  
Claudia Draxl ◽  
Matthias Scheffler
Keyword(s):  
Big Data ◽  
Materials Science ◽  
Data Driven ◽  
Image Position

Abstract

Data-driven visualization schema of a materials informatics curriculum: Convergence of materials science and information science

MRS Advances ◽  
2020 ◽  
Vol 5 (7) ◽  
pp. 293-303
Author(s):  
Erik Einarsson ◽  
Olga Wodo ◽  
Prathima C. Nalam ◽  
Scott R. Broderick ◽  
Kristofer G. Reyes ◽  
...  
Keyword(s):  
Data Science ◽  
Information Science ◽  
Materials Science ◽  
Student Assessment ◽  
Critical Element ◽  
Illustrative Case ◽  
Materials Informatics ◽  
Curriculum Assessment ◽  
Illustrative Case Study

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.

scholarly journals Big Data of Materials Science: Critical Role of the Descriptor

2015 ◽  
Vol 114 (10) ◽  
Author(s):  
Luca M. Ghiringhelli ◽  
Jan Vybiral ◽  
Sergey V. Levchenko ◽  
Claudia Draxl ◽  
Matthias Scheffler
Keyword(s):  
Big Data ◽  
Materials Science ◽  
Critical Role

scholarly journals Big data are shaping the future of materials science

MRS Bulletin ◽  
2013 ◽  
Vol 38 (8) ◽  
pp. 594-595 ◽  
Author(s):  
Ashley A. White
Keyword(s):  
Big Data ◽  
Materials Science ◽  
The Future

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

MRS Advances ◽  
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.

scholarly journals Big data and machine learning for materials science

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Jose F. Rodrigues ◽  
Larisa Florea ◽  
Maria C. F. de Oliveira ◽  
Dermot Diamond ◽  
Osvaldo N. Oliveira
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Materials Science ◽  
Leading Edge ◽  
Sensor Data ◽  
Research Fields ◽  
Research And Innovation ◽  
Potential Benefits ◽  
The Cost ◽  
Different Sources

AbstractHerein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

Sign in / Sign up

Export Citation Format

Share Document