Machine Learning-Based Detection of Graphene Defects with Atomic Precision

AbstractDefects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative approach is to observe the thermal vibration properties of the graphene sheet, which reflects defect information but in an implicit fashion. Machine learning, an emerging data-driven approach that offers solutions to learning hidden patterns from complex data, has been extensively applied in material design and discovery problems. In this paper, we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration features. Two prediction strategies are developed: an atom-based method which constructs data by atom indices, and a domain-based method which constructs data by domain discretization. Results show that while the atom-based method is capable of detecting a single-atom vacancy, the domain-based method can detect an unknown number of multiple vacancies up to atomic precision. Both methods can achieve approximately a 90% prediction accuracy on the reserved data for testing, indicating a promising extrapolation into unseen future graphene configurations. The proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

2017 ◽

Author(s):

Benjamin Sanchez-Lengeling ◽

Carlos Outeiral ◽

Gabriel L. Guimaraes ◽

Alan Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Learning Community ◽

Chemical Species ◽

Material Design ◽

Organic Photovoltaic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

Current Genomics ◽

10.2174/1389202921999201224110101 ◽

2020 ◽

Vol 21 ◽

Author(s):

Sukanya Panja ◽

Sarra Rahem ◽

Cassandra J. Chu ◽

Antonina Mitrofanova

Keyword(s):

Machine Learning ◽

Missing Values ◽

Therapeutic Response ◽

Patient Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Complex Data ◽

Human Machine Interaction ◽

Data Repositories ◽

Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽

10.3390/s21144736 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4736

Author(s):

Sk. Tanzir Mehedi ◽

Adnan Anwar ◽

Ziaur Rahman ◽

Kawsar Ahmed

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Real Time ◽

Transfer Learning ◽

Security Requirements ◽

Detection Accuracy ◽

Area Network ◽

Complex Data ◽

Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Data Science and Engineering ◽

10.1007/s41019-021-00155-3 ◽

2021 ◽

Author(s):

Yun Peng ◽

Byron Choi ◽

Jianliang Xu

Keyword(s):

Machine Learning ◽

Combinatorial Optimization ◽

Graph Embedding ◽

Partial Solution ◽

Complex Data ◽

Learning Methods ◽

Graph Learning ◽

Second Stage ◽

End To End ◽

Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

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A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys

Scientific Reports ◽

10.1038/s41598-021-90237-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jin-Woong Lee ◽

Chaewon Park ◽

Byung Do Lee ◽

Joonseo Park ◽

Nam Hoon Goo ◽

...

Keyword(s):

Machine Learning ◽

Feature Space ◽

Research Strategy ◽

Nsga Ii ◽

Steel Alloys ◽

Non Linear ◽

Data Driven Approach ◽

Processing Information ◽

Set Up ◽

World Industry

AbstractPredicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established theoretical and empirical models. A data-driven approach should be a fundamental exercise when making YS/UTS predictions. For this study, we collected 16 descriptors (attributes) that implicate the compositional and processing information and the corresponding YS/UTS values for 5473 thermo-mechanically controlled processed (TMCP) steel alloys. We set up an integrated machine-learning (ML) platform consisting of 16 ML algorithms to predict the YS/UTS based on the descriptors. The integrated ML platform involved regularization-based linear regression algorithms, ensemble ML algorithms, and some non-linear ML algorithms. Despite the dirty nature of most real-world industry data, we obtained acceptable holdout dataset test results such as R2 > 0.6 and MSE < 0.01 for seven non-linear ML algorithms. The seven fully trained non-linear ML models were used for the ensuing ‘inverse design (prediction)’ based on an elitist-reinforced, non-dominated sorting genetic algorithm (NSGA-II). The NSGA-II enabled us to predict solutions that exhibit desirable YS/UTS values for each ML algorithm. In addition, the NSGA-II-driven solutions in the 16-dimensional input feature space were visualized using holographic research strategy (HRS) in order to systematically compare and analyze the inverse-predicted solutions for each ML algorithm.

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First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

Journal of Materials Chemistry A ◽

10.1039/d1ta00751c ◽

2021 ◽

Author(s):

Mohammad Zafari ◽

Arun S. Nissimagoudar ◽

Muhammad Umer ◽

Geunsik Lee ◽

Kwang S. Kim

Keyword(s):

Machine Learning ◽

Nitrogen Fixation ◽

First Principles ◽

Conjugated Polymer ◽

2D Materials ◽

Single Atom ◽

Vacancy Defects ◽

Catalytic Activities ◽

Polymer Supported ◽

New Machine

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

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Machine learning: The trends of developing high-efficiency single-atom materials

Chem Catalysis ◽

10.1016/j.checat.2021.04.005 ◽

2021 ◽

Vol 1 (1) ◽

pp. 24-26

Author(s):

Jiarui Yang ◽

Wen-Hao Li ◽

Dingsheng Wang

Keyword(s):

Machine Learning ◽

High Efficiency ◽

Single Atom

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Unravelling the Origin of Bifunctional OER/ORR Activity for Single-Atom Catalysts Supported on C2N by DFT and Machine Learning

Journal of Materials Chemistry A ◽

10.1039/d1ta04256d ◽

2021 ◽

Author(s):

Yiran Ying ◽

Ke Fan ◽

Xin Luo ◽

Jinli Qiao ◽

Haitao Huang

Keyword(s):

Machine Learning ◽

Fuel Cells ◽

Oxygen Evolution ◽

High Performance ◽

Reduction Reaction ◽

Single Atom ◽

Orr Activity

Designing high-performance bifunctional oxygen evolution/reduction reaction (OER/ORR) catalysts is a newly emerged topic with wide applications in metal-air batteries and fuel cells. Herein, we report a group of (27) single-atom...

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Heart disease prediction using machine learning techniques : a survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10557 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 684 ◽

Cited By ~ 12

Author(s):

V V. Ramalingam ◽

Ayantan Dandapath ◽

M Karthik Raja

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Complex Data ◽

Learning Techniques ◽

Vector Machines ◽

Supervised Learning Algorithms ◽

Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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Automated Feature Selection and Classification for High-Dimensional Biomedical Data

10.21203/rs.3.rs-563410/v1 ◽

2021 ◽

Author(s):

Tammo P.A. Beishuizen ◽

Joaquin Vanschoren ◽

Peter A.J. Hilbers ◽

Dragan Bošnački

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Automated System ◽

Complex Data ◽

Biomedical Data ◽

Selection Methods ◽

Model Predictions ◽

Automated Machine Learning ◽

Feature Selection Techniques ◽

Best Fit

Abstract Background: Automated machine learning aims to automate the building of accurate predictive models, including the creation of complex data preprocessing pipelines. Although successful in many fields, they struggle to produce good results on biomedical datasets, especially given the high dimensionality of the data. Result: In this paper, we explore the automation of feature selection in these scenarios. We analyze which feature selection techniques are ideally included in an automated system, determine how to efficiently find the ones that best fit a given dataset, integrate this into an existing AutoML tool (TPOT), and evaluate it on four very different yet representative types of biomedical data: microarray, mass spectrometry, clinical and survey datasets. We focus on feature selection rather than latent feature generation since we often want to explain the model predictions in terms of the intrinsic features of the data. Conclusion: Our experiments show that for none of these datasets we need more than 200 features to accurately explain the output. Additional features did not increase the quality significantly. We also find that the automated machine learning results are significantly improved after adding additional feature selection methods and prior knowledge on how to select and tune them.

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