Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology

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Abstract

Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the $K$ -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.

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Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

Hironao Yamada, Chang Liu, Stephen Wu … (2019)

There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called “transfer learning” has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140 000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

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Deep materials informatics: Applications of deep learning in materials science

Ankit Agrawal, Alok Choudhary (2019)

The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.