scholarly journals Tool for Designing Breakthrough Discovery in Materials Science

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6946
Author(s):  
Michiko Yoshitake
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Calculated Data ◽  
Database Search ◽  
Search System ◽  
Research Perspective ◽  
Computer Aided ◽  
Scientific Principles ◽  
Materials Research ◽  
Prototype Software

A database of material property relationships, which serves as a scientific principles database, and a database search system are proposed and developed. The use of this database can support a broader research perspective, which is increasingly important in the era of automated computer-aided experimentation and machine learning of experimental and calculated data. Examples of the wider use of scientific principles in materials research are presented. The database and its advantages are described. An implementation of the proposed database and search system as a prototype software is reported. The usefulness of the database and search system is demonstrated by an example of a surprising but reasonable discovery.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals The search for new materials and the role of novel processing routes

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Peter J. Wellmann
Keyword(s):  
Materials Science ◽  
Research Society ◽  
Subsequent Treatment ◽  
European Council ◽  
Trial And Error ◽  
Human History ◽  
New Materials ◽  
Materials Research ◽  
Novel Processing

AbstractThroughout human history, most further developments or new achievements were accompanied by new materials or new processes that enabled the technologic progress. With concrete devices and applications in mind, synthesis and subsequent treatment of materials naturally went along with the progress. The aim of the underlying article is to spot the role of optimization, of discovery, of trial-and-error approaches, of fundamentals and curiosity driven design and development. In a consecutive examination, five missions addressing the challenges facing our world (identified by the European Council) will be cross linked with seven topical areas from materials science defined by the European Materials Research Society. The scope of this examination is to identify approaches and methods to further develop and innovate materials which form the basis of the anticipated solutions.

Catalyze Materials Science with Machine Learning

2021 ◽  
pp. 1151-1171
Author(s):  
Jaehyun Kim ◽  
Donghoon Kang ◽  
Sangbum Kim ◽  
Ho Won Jang
Keyword(s):  
Machine Learning ◽  
Materials Science

scholarly journals Two-step machine learning enables optimized nanoparticle synthesis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Flore Mekki-Berrada ◽  
Zekun Ren ◽  
Tan Huang ◽  
Wai Kuan Wong ◽  
Fang Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Materials Science ◽  
Nanoparticle Synthesis ◽  
Bayesian Optimization ◽  
Absorbance Spectrum ◽  
Nanomaterial Synthesis ◽  
Target Spectrum ◽  
Colour Palette ◽  
Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

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