scholarly journals Fe–Al–Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials Development

2021 ◽  
Author(s):  
Yoshiki Takagiwa ◽  
Zhufeng Hou ◽  
Koji Tsuda ◽  
Teruyuki Ikeda ◽  
Hiroyasu Kojima
Keyword(s):  
Machine Learning ◽  
Diffusion Couple ◽  
Materials Development

scholarly journals Applying machine learning to accelerate new materials development

2018 ◽  
Vol 48 (10) ◽  
pp. 107001 ◽  
Author(s):  
Wei WU ◽  
Qiang SUN
Keyword(s):  
Machine Learning ◽  
New Materials ◽  
Materials Development

scholarly journals Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

Joule ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 1410-1420 ◽  
Author(s):  
Juan-Pablo Correa-Baena ◽  
Kedar Hippalgaonkar ◽  
Jeroen van Duren ◽  
Shaffiq Jaffer ◽  
Vijay R. Chandrasekhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance Computing ◽  
High Performance ◽  
Materials Development ◽  
Performance Computing

scholarly journals The role of machine learning in scientific workflows

2019 ◽  
Vol 33 (6) ◽  
pp. 1128-1139 ◽  
Author(s):  
Ewa Deelman ◽  
Anirban Mandal ◽  
Ming Jiang ◽  
Rizos Sakellariou
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Autonomous Vehicles ◽  
Workflow Management ◽  
Scientific Productivity ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Materials Development ◽  
Smart Agriculture

Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

2021 ◽  
pp. 2101474
Author(s):  
Chade Lv ◽  
Xin Zhou ◽  
Lixiang Zhong ◽  
Chunshuang Yan ◽  
Madhavi Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Materials Development ◽  
State Prediction

scholarly journals Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Yuma Iwasaki ◽  
Ryohto Sawada ◽  
Valentin Stanev ◽  
Masahiko Ishida ◽  
Akihiro Kirihara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermoelectric Materials ◽  
Material Science ◽  
Scientific Discovery ◽  
Functional Materials ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Materials Development ◽  
Interpretable Machine Learning ◽  
Hierarchical Mixture Of Experts

Abstract Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.

scholarly journals Machine learning: Accelerating materials development for energy storage and conversion

InfoMat ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 553-576 ◽  
Author(s):  
An Chen ◽  
Xu Zhang ◽  
Zhen Zhou
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Energy Storage And Conversion ◽  
Materials Development

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Quantitative x-ray microanalysis and thickness determination using ζ factor

1996 ◽  
Vol 54 ◽  
pp. 580-581
Author(s):  
M. Watanabe ◽  
Z. Horita ◽  
M. Nemoto
Keyword(s):  
Diffusion Couple ◽  
Extrapolation Method ◽  
Thin Specimen ◽  
Beam Position ◽  
X Ray ◽  
Thickness Determination ◽  
Experimental Limitation ◽  
X Ray Absorption

X-ray absorption in quantitative x-ray microanalysis of thin specimens may be corrected without knowledge of thickness when the extrapolation method or the differential x-ray absorption (DXA) method is used. However, there is an experimental limitation involved in each method. In this study, a method is proposed to overcome such a limitation. The method is developed by introducing the ζ factor and by combining the extrapolation method and DXA method. The method using the ζ factor, which is called the ζ-DXA method in this study, is applied to diffusion-couple experiments in the Ni-Al system.For a thin specimen where incident electrons are fully transparent, the characteristic x-ray intensity generated from a beam position, I, may be represented as I = (NρW/A)Qωaist.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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