A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

Abstract

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

Modelling mechanisms for measurable and detection based on artificial intelligence

“…It is difficult for conventional linear and non-linear operations to handle a complicated relationship between the input and result of material properties. However, ML techniques can now effectively represent these complex interactions [11]. Traditional linear and non-linear correlation approaches have difficulty handling a complicated connection between the input and outputs of material attributes.…”

“…The demand for AI in the modelling and investigation of novel ceramic materials is rising as a result of its effective applicability for creating efficiency and performance. It is anticipated that materials development based on AI analysis will produce novel materials and lower the development cost both in terms of time and materials [4], [5]. The scientific community has, however, noted numerous constraints on the discovery and use of improved materials based on advanced machine learning and AI approaches.…”

Deep learning in bioinformatics

“…Appropriate data preprocessing and normalization are critical for ensuring that the input data are suitable for deep-learning models ( Imran et al, 2022 ). Some bioinformatics analyses involve various steps, such as sequence alignment, quality control, feature extraction, and data transformation.…”

Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT

“…Recently, applications for deep learning have been found in diagnosis and prediction across various domains [ 7 , 13 , 14 ]. Furthermore, deep learning methods notably impact classification accuracy in numerous medical tasks [ 15 , 16 ].…”