scholarly journals Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Coatings ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Simen Ringdahl ◽  
Senbo Xiao ◽  
Jianying He ◽  
Zhiliang Zhang
Keyword(s):  
Machine Learning ◽  
Adhesion Strength ◽  
Liquid Layer ◽  
Interatomic Potential ◽  
Rough Surfaces ◽  
Md Simulations ◽  
Support Vector ◽  
Interatomic Potentials ◽  
Quasi Liquid Layer ◽  
Ice Adhesion

It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) simulations and machine learning (ML), were utilized to study the nanoscale intrinsic ice adhesion strength on rough surfaces. A systematic algorithm for making random rough surfaces was developed and the surfaces were tested for their ice adhesion strength, with varying interatomic potentials. Using MD simulations, the intrinsic ice adhesion strength was found to be significantly lower on rougher surfaces, which was attributed to the lubricating effect of a thin quasi-liquid layer. An increase in the substrate–ice interatomic potential increased the thickness of the quasi-liquid layer on rough surfaces. Two different ML algorithms, regression and classification, were trained using the results from the MD simulations, with support vector machines (SVM) emerging as the best for classifying. The ML approach showed an encouraging prediction accuracy, and for the first time shed light on using ML for anti-icing surface design. The findings provide a better understanding of the role of nanoscale roughness in intrinsic ice adhesion and suggest that ML can be a powerful tool in finding materials with a low ice adhesion strength.

scholarly journals Construction of an n-Body Potential for Revealing the Atomic Mechanism for Direct Alloying of Immiscible Tungsten and Copper

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 5988
Author(s):  
Tao Zeng ◽  
Fei Li ◽  
Yuan Huang
Keyword(s):  
High Temperature ◽  
Interatomic Potential ◽  
Diffusion Mechanism ◽  
Critical Role ◽  
Experimental Studies ◽  
Md Simulations ◽  
Interatomic Potentials ◽  
Direct Alloying ◽  
Materials Used ◽  
Body Potential

W-Cu laminated composites are critical materials used to construct nuclear fusion reactors, and it is very important to obtain direct alloying between W and Cu at the W/Cu interfaces of the composites. Our previous experimental studies showed that it is possible to overcome the immiscibility between W and Cu and obtain direct alloying when the alloying temperature is close to the melting point of Cu. Because the W-Cu interatomic potentials published thus far cannot accurately reproduce the alloying behaviors of immiscible W and Cu, an interatomic potential suitable for the W-Cu system has been constructed in the present study. Based on this potential, direct alloying between W and Cu at high temperature has been verified, and the corresponding diffusion mechanism has been studied, through molecular dynamics (MD) simulations. The results indicate that the formation of an amorphous Cu layer at the W/Cu interface plays a critical role in alloying because it allows Cu atoms to diffuse into W. The simulation results for direct alloying between W and Cu can be verified by experimental results and transmission electron microscopy observations. This indicates that the constructed W-Cu potential can correctly model the high-temperature performance of the W-Cu system and the diffusion mechanism of direct alloying between W and Cu.

scholarly journals Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tongqi Wen ◽  
Rui Wang ◽  
Lingyu Zhu ◽  
Linfeng Zhang ◽  
Han Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Mechanical Response ◽  
Interatomic Potential ◽  
Dislocation Core ◽  
General Purpose ◽  
Direct Access ◽  
Interatomic Potentials ◽  
Atomistic Modelling ◽  
Complex Phenomena

AbstractLarge scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

scholarly journals Potential neutralizing antibodies discovered for novel corona virus using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rishikesh Magar ◽  
Prakarsh Yadav ◽  
Amir Barati Farimani
Keyword(s):  
Machine Learning ◽  
Neutralizing Antibodies ◽  
Md Simulations ◽  
Support Vector ◽  
Inhibitory Antibody ◽  
Clinical Patient ◽  
Multilayered Perceptron ◽  
The Stability ◽  
High Throughput Manner ◽  
Neutralization Response

AbstractThe fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

scholarly journals Hybrid machine learning assisted quantification of the compound internal and external uncertainties of graphene: Towards inclusive analysis and design

2021 ◽  
Author(s):  
Kritesh K. Gupta ◽  
Tanmoy Mukhopadhyay ◽  
Lintu Roy ◽  
Sudip Dey
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Interatomic Potential ◽  
Md Simulations ◽  
First Principle ◽  
Reliability Of Results ◽  
Analysis And Design ◽  
Hybrid Machine ◽  
Inclusive Analysis

Reliability of results derived from molecular dynamics (MD) simulations depends on the adopted interatomic potential (IP), which is mathematically fitted to the data obtained from first principle approaches or experiments....

scholarly journals Water and Ice Adhesion to Solid Surfaces: Common and Specific, the Impact of Temperature and Surface Wettability

Coatings ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 648
Author(s):  
Kirill A. Emelyanenko ◽  
Alexandre M. Emelyanenko ◽  
Ludmila B. Boinovich
Keyword(s):  
Adhesion Strength ◽  
Crucial Role ◽  
Surface Forces ◽  
Solid Surfaces ◽  
Superhydrophobic Surfaces ◽  
Ice Accumulation ◽  
Quasi Liquid Layer ◽  
The Impact ◽  
Ice Adhesion

Ice adhesion plays a crucial role in the performance of materials under outdoor conditions, where the mitigation of snow and ice accumulation or spontaneous shedding of solid water precipitations are highly desirable. In this brief review we compare the adhesion of water and ice to different surfaces and consider the mechanisms of ice adhesion to solids basing on the surface forces analysis. The role of a premelted or quasi-liquid layer (QLL) in the ice adhesion is discussed with the emphasis on superhydrophobic surfaces, and the temperature dependence of ice adhesion strength is considered with an account of QLL. We also very briefly mention some recent methods for the measurement of ice adhesion strength to the icephobic engineering materials outlining the problems which remain to be experimentally solved.

Development of Neural-Network Interatomic Potentials for Structural Materials

2016 ◽  
Vol 258 ◽  
pp. 69-72
Author(s):  
Ryo Kobayashi ◽  
Tomoyuki Tamura ◽  
Ichiro Takeuchi ◽  
Shuji Ogata
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Md Simulation ◽  
Interatomic Potential ◽  
Basis Functions ◽  
Interatomic Potentials ◽  
Machine Learning Method ◽  
Learning Method ◽  
New Approach ◽  
The Neural Network

The validity of the molecular dynamics (MD) simulation is highly dependent on the accuracy or reproducibility of interatomic potentials used in the MD simulation. The neural-network (NN) interatomic potential is one of promising interatomic potentials based on machine-learning method. However, there are some parameters that should be determined heuristically before making the NN potential, such as the shape and number of basis functions. We have developed a new approach to select only relevant basis functions from a lot of candidates systematically and less heuristically without loosing the accuracy of the potential. The present NN potential for Si system shows very good agreements with the results obtained using ab-initio calculations.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Phum Tachachartvanich ◽  
Denis Fourches ◽  
Anatoly Soshilov ◽  
Jennifer C.Y. Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Androgen Receptor ◽  
Endocrine Disruptors ◽  
Hormone Receptors ◽  
Steroid Hormone Receptors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Polyfluoroalkyl Substances ◽  
Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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