Machine learning in nuclear materials research

2022 ◽  
Vol 26 (2) ◽  
pp. 100975
Author(s):  
Dane Morgan ◽  
Ghanshyam Pilania ◽  
Adrien Couet ◽  
Blas P. Uberuaga ◽  
Cheng Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nuclear Materials ◽  
Materials Research

scholarly journals The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research

2020 ◽  
Vol 176 ◽  
pp. 109544 ◽  
Author(s):  
Ryan Jacobs ◽  
Tam Mayeshiba ◽  
Ben Afflerbach ◽  
Luke Miles ◽  
Max Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Driven ◽  
Materials Simulation ◽  
Materials Research

scholarly journals Machine Learning Analysis of Raman Spectra of MoS2

Nanomaterials ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2223
Author(s):  
Yu Mao ◽  
Ningning Dong ◽  
Lei Wang ◽  
Xin Chen ◽  
Hongqiang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectra ◽  
2D Materials ◽  
Optical Microscope ◽  
Nucleation Site ◽  
Optical Contrast ◽  
Continuous Film ◽  
Materials Research ◽  
High Recognition Accuracy ◽  
Learning Analysis

Defects introduced during the growth process greatly affect the device performance of two-dimensional (2D) materials. Here we demonstrate the applicability of employing machine-learning-based analysis to distinguish the monolayer continuous film and defect areas of molybdenum disulfide (MoS2) using position-dependent information extracted from its Raman spectra. The random forest method can analyze multiple Raman features to identify samples, making up for the problem of not being able to effectively identify by using just one certain variable with high recognition accuracy. Even some dispersed nucleation site defects can be predicted, which would commonly be ignored under an optical microscope because of the lower optical contrast. The successful application for classification and analysis highlights the potential for implementing machine learning to tap the depth of classical methods in 2D materials research.

Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

2020 ◽  
Author(s):  
Kate Higgins ◽  
Sai Mani Valleti ◽  
Maxim Ziatdinov ◽  
Sergei Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Machine Learning ◽  
Optimization Methods ◽  
Combinatorial Synthesis ◽  
Ambient Conditions ◽  
Term Stability ◽  
Long Term Stability ◽  
Composition Space ◽  
Materials Research ◽  
The Stability

<p>Hybrid organic-inorganic perovskites have attracted immense interest as a promising material for the next-generation solar cells; however, issues regarding long-term stability still require further study. Here, we develop automated experimental workflow based on combinatorial synthesis and rapid throughput characterization to explore long-term stability of these materials in ambient conditions, and apply it to four model perovskite systems: MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbBr<sub>3</sub>, MA<sub>x</sub>FA<sub>y</sub>Cs<sub>1-x-y</sub>PbI<sub>3</sub>, (Cs<sub>x</sub>FA<sub>y</sub>MA<sub>1-x-y</sub>Pb(Br<sub>x+y</sub>I<sub>1-x-y</sub>)<sub>3</sub>) and (Cs<sub>x</sub>MA<sub>y</sub>FA<sub>1-x-y</sub>Pb(I<sub>x+y</sub>Br<sub>1-x-y</sub>)<sub>3</sub>). We also develop a machine learning-based workflow to quantify the evolution of each system as a function of composition based on overall changes in photoluminescence spectra, as well as specific peak positions and intensities. We find the stability dependence on composition to be extremely non-uniform within the composition space, suggesting the presence of potential preferential compositional regions. This proposed workflow is universal and can be applied to other perovskite systems and solution-processable materials. Furthermore, incorporation of experimental optimization methods, e.g., those based on Gaussian Processes, will enable the transition from combinatorial synthesis to guide materials research and optimization.</p>

scholarly journals Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Masashi Kawaguchi ◽  
Kenji Tanabe ◽  
Keisuke Yamada ◽  
Takuya Sawa ◽  
Shun Hasegawa ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Magnetic Anisotropy ◽  
Magnetic Domain ◽  
Exchange Constant ◽  
Magnetic Memory ◽  
Materials Informatics ◽  
Materials Research ◽  
Neural Network System

AbstractMachine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii–Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.

Machine Learning Approaches for Thermoelectric Materials Research

2019 ◽  
Vol 30 (5) ◽  
pp. 1906041 ◽  
Author(s):  
Tian Wang ◽  
Cheng Zhang ◽  
Hichem Snoussi ◽  
Gang Zhang
Keyword(s):  
Machine Learning ◽  
Thermoelectric Materials ◽  
Learning Approaches ◽  
Materials Research

scholarly journals Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

2021 ◽  
Author(s):  
Enric Grau-Luque ◽  
Ikram Anefnaf ◽  
Nada Benhaddou ◽  
Robert Fonoll-Rubio ◽  
Ignacio Becerril-Romero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Complex Systems ◽  
Defect Formation ◽  
Cell Performance ◽  
Combinatorial Analysis ◽  
Learning Approaches ◽  
Solar Cell Performance ◽  
Materials Research ◽  
Photovoltaic Technology

This work provides insights for understanding and further developing the Cu2ZnGeSe4 photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.

Radiation Portal Monitors: Problems and Development Prospects

2015 ◽  
Vol 1084 ◽  
pp. 702-707
Author(s):  
Artem V. Dudkin
Keyword(s):  
New Technologies ◽  
Radiation Monitoring ◽  
Nuclear Materials ◽  
Nuclear Facilities ◽  
Improve Design ◽  
Special Nuclear Materials ◽  
Protection Systems ◽  
Materials Research ◽  
Monitoring Technologies

The problem of non-proliferation of special nuclear materials remains actual, so it is required to study new technologies and perspective materials for ensuring radiation monitoring at checkpoints. In this article application of radiation portal monitors of special nuclear materials is reviewed considering features of physical protection systems of nuclear facilities. Russian and foreign experience of radiation portal monitors design is summarized. A number of means to improve design of radiation portal monitors of special nuclear materials were offered. Also problem and important role of materials research is indicated considering development of radiation monitoring technologies.

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