scholarly journals Machine learning to predict quasicrystals from chemical compositions

2021 ◽  
Author(s):  
Chang Liu ◽  
Erina Fujita ◽  
Yukari Katsura ◽  
Yuki Inada ◽  
Asuka Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Predictive Power ◽  
Chemical Compositions ◽  
Empirical Equations ◽  
Long Range Order ◽  
Input Output ◽  
New Class ◽  
Phase Prediction ◽  
Validation Experiments

Abstract Quasicrystals have emerged as a new class of solid-state materials that have long-range order without periodicity, exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, hundreds of new quasicrystals have been found, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has slowed in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, we show that the discovery of new quasicrystals can be accelerated with a simple machine learning workflow. With a list of the chemical compositions of known quasicrystals, approximant crystals, and ordinary crystals, we trained a prediction model to solve the three-class classification task and evaluated its predictability compared to the observed phase diagrams of ternary aluminum systems. The validation experiments strongly support the superior predictive power of machine learning, with the precision and recall of the phase prediction task reaching approximately 0.793 and 0.714, respectively. Furthermore, analyzing the input--output relationships black-boxed into the model, we identified nontrivial empirical equations interpretable by humans that describe conditions necessary for quasicrystal formation.

scholarly journals Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Haoyue Guo ◽  
Qian Wang ◽  
Annika Stuke ◽  
Alexander Urban ◽  
Nongnuch Artrith
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
First Principles ◽  
Reference Method ◽  
Atomic Scale ◽  
Chemical Compositions ◽  
Computationally Efficient ◽  
Battery Materials ◽  
Scale Modeling ◽  
Solid State Batteries

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

scholarly journals Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Meisam Ghasedi ◽  
Maryam Sarfjoo ◽  
Iraj Bargegol
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Logit Model ◽  
Predictive Power ◽  
Dominant Role ◽  
Learning Approaches ◽  
Lighting Condition ◽  
Rainy Weather ◽  
Pedestrian Accidents ◽  
Effective Principal

AbstractThe purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

scholarly journals Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

2021 ◽  
pp. 1639-1648
Author(s):  
Yu-Ting Chen ◽  
Marc Duquesnoy ◽  
Darren H. S. Tan ◽  
Jean-Marie Doux ◽  
Hedi Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
High Quality ◽  
Solid State Electrolyte

Application of machine learning-based models to boost the predictive power of the SPAN index

2021 ◽  
pp. 1-11
Author(s):  
Chen-Chih Chung ◽  
Oluwaseun Adebayo Bamodu ◽  
Chien-Tai Hong ◽  
Lung Chan ◽  
Hung-Wen Chiu
Keyword(s):  
Machine Learning ◽  
Predictive Power

First validation experiments of the prototype solid state RF system for IFMIF-DONES

2021 ◽  
Vol 168 ◽  
pp. 112396
Author(s):  
Cristina de la Morena ◽  
David Regidor ◽  
Daniel Iriarte ◽  
Francisco Sierra ◽  
Eduardo Ugarte ◽  
...  
Keyword(s):  
Solid State ◽  
Rf System ◽  
Validation Experiments

scholarly journals Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4671
Author(s):  
Danpeng Cheng ◽  
Wuxin Sha ◽  
Linna Wang ◽  
Shun Tang ◽  
Aijun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Life Prediction ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Cycle Life ◽  
Quantitative Prediction ◽  
Test Accuracy ◽  
Machine Learning Algorithm ◽  
Discharge Data

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

scholarly journals High-Performance Multi-Stream Management for SSDs

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 486
Author(s):  
Yongjae Chun ◽  
Kyeore Han ◽  
Youpyo Hong
Keyword(s):  
Solid State ◽  
High Performance ◽  
Data Centers ◽  
Consumer Electronics ◽  
Input Output ◽  
Solid State Drives ◽  
Stream Management ◽  
Group Data ◽  
Separate Area

Owing to their advantages over hard disc drives (HDDs), solid-state drives (SSDs) are widely used in many applications, including consumer electronics and data centers. As erase operations are feasible only in block units, modification or deletion of pages cause invalidation of the pages in their corresponding blocks. To reclaim these invalid pages, the valid pages in the block are copied to other blocks, and the block with the invalid pages is initialized, which adversely affects the performance and durability of the SSD. The objective of a multi-stream SSD is to group data by their expected lifetimes and store each group of data in a separate area called a stream to minimize the frequency of wasteful copy-back and initialization operations. In this paper, we propose an algorithm that groups the data based on input/output (I/O) types and rewrite frequency, which show significant improvements over existing multi-stream algorithms not only for performance but also for effectiveness in covering most applications.

scholarly journals Machine learning for a rapid discrimination of ginseng cultivation age using 1H-NMR spectra

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Wonho Lee ◽  
Dahye Yoon ◽  
Seohee Ma ◽  
Dae Young Lee ◽  
Jae Won Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Nmr Spectroscopy ◽  
Magic Angle Spinning ◽  
Magic Angle ◽  
Systematic Classification ◽  
New Class ◽  
Angle Spinning ◽  
Rapid Discrimination ◽  
Ginseng Cultivation

Abstract The scientific and systematic classification of cultivation age is important for preventing age falsification and ensuring the quality of ginseng. Therefore, we applied deep learning to classify the cultivation age of ginseng. Deep learning, which is based on an artificial neural network, is one of the new class of models for machine learning, and is state-of-the-art. It is a powerful tool and has been used to solve complex problems in many fields. In the present study, powdered samples of 4-, 5-, and 6-year-old ginseng were measured using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. NMR data were analyzed with deep learning and partial least-squares discriminant analysis (PLS-DA) to improve accuracy. The accuracy of the PLS-DA was 87.1% and the accuracy of the deep learning model was 93.9%. NMR spectroscopy with deep learning can be a useful tool for discrimination of ginseng cultivation age.

scholarly journals Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

2017 ◽  
Vol 79 (02) ◽  
pp. 123-130 ◽  
Author(s):  
Whitney Muhlestein ◽  
Dallin Akagi ◽  
Justiss Kallos ◽  
Peter Morone ◽  
Kyle Weaver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Patient Outcomes ◽  
Predictive Power ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Motor Deficit ◽  
Ensemble Model ◽  
Algorithm Selection ◽  
Discharge Disposition

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

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