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Machine learning workflow for microparticle composite thin-film process–structure linkages
Microparticle composite thin films (MCTFs) have applications in a variety of fields, ranging from water filtration, to advanced energy storage, to...
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Materials Integration—Data-Driven Approach to Materials Design Using Simulation and Database
Materials integration (MI) is an approach that links the four elements of materials, Processing, Structure, Properties and Performance, on a computer...
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MICRO2D: A Large, Statistically Diverse, Heterogeneous Microstructure Dataset
The availability of large, diverse datasets has enabled transformative advances in a wide variety of technical fields by unlocking data scientific...
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Prediction of mechanical properties for deep drawing steel by deep learning
At present, iron and steel enterprises mainly use “after spot test ward” to control final product quality. However, it is impossible to realize...
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From Data Science to Materials Data Science
In the previous chapter, we saw that data science has ancient roots in many disciplines. For example, it is closely related to statistical theory and...
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MatHub-2d: A database for transport in 2D materials and a demonstration of high-throughput computational screening for high-mobility 2D semiconducting materials
Two-dimensional materials (2DMs) provide remarkable physical and chemical properties not found in other classes of materials. A crucial property for...
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Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential
Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive...
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Validation of graph sequence clusters through multivariate analysis: application to Rovash scripts
This paper introduces the concept of pattern systems that evolve, with a focus on scripts, a specific type of pattern system. The study analyses the...
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Machine Learning
Machine learning is useful to identify rules hidden in given data and to predict unknown data using the identified rules. It has been increasingly...
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Material Agnostic Data-Driven Framework to Develop Structure-Property Linkages
The concept of Integrated Computational Materials Engineering (ICME) is aimed at accelerating the development and insertion of new materials in...
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Structural-Order Analysis Based on Applied Mathematics
The development of experimental and simulation technologies has afforded us access to material science data on a more massive scale than that in the...
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Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning
Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials...
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A Bayesian framework for materials knowledge systems
This prospective offers a new Bayesian framework that could guide the systematic application of the emerging toolsets of machine learning in the...
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Synthesis Strategies for Organoselenium Compounds and Their Potential Applications in Human Life
This article describes the prime role of selenium (Se) and its compounds in mammalian biochemical systems, performing diverse functions like...
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Microstructural informatics for accelerating the discovery of processing–microstructure–property relationships
The study of microstructure–property relationships and processing history leading to those relationships is at the core of materials engineering. The...
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Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications
Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion of...
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Number Density Descriptor on Extended-Connectivity Fingerprints Combined with Machine Learning Approaches for Predicting Polymer Properties
We developed a new type of polymer descriptor based on Extended Connectivity Fingerprints. The number densities, that are substructure numbers...
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Role of materials data science and informatics in accelerated materials innovation
The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal...
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Accelerating the discovery of materials for clean energy in the era of smart automation
The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing...
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Microalgal Biomass of Industrial Interest: Methods of Characterization
Microalgae represent a new source of biomass for many applications. The advantage of microalgae over higher plants is their high productivities. The...