Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design. 

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Recent advances and applications of deep learning methods in materials science

Although applications of additive manufacturing (AM) have been significantly increased in recent years, its broad application in several industries is still under progress. AM also known as three-dimensional (3D) printing is layer by layer manufacturing process which can be used for fabrication of geometrically complex customized functional end-use products. Since AM processing parameters have significant effects on the performance of the printed parts, it is necessary to tune these parameters which is a difficult task. Today, different artificial intelligence techniques have been utilized to optimize AM parameters and predict mechanical behavior of 3D-printed components. In the present study, applications of machine learning (ML) in prediction of structural performance and fracture of additively manufactured components has been presented. This study first outlines an overview of ML and then summarizes its applications in AM. The main part of this review, focuses on applications of ML in prediction of mechanical behavior and fracture of 3D-printed parts. To this aim, previous research works which investigated application of ML in characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed. Moreover, the review and analysis indicate limitations, challenges, and perspectives for industrial applications of ML in the field of AM. Considering advantages of ML increase in applications of ML in optimization of 3D printing parameters, prediction of mechanical performance, and evaluation of 3D-printed products is expected.

Machine learning in predicting mechanical behavior of additively manufactured parts

Recent far-reaching advances in synthetic biology have yielded exciting tools for the creation of new materials. Conversely, advances in the fundamental understanding of soft-condensed matter, polymers and biomaterials offer new avenues to extend the reach of synthetic biology. The broad and exciting range of possible applications have substantial implications to address grand challenges in health, biotechnology and sustainability. Despite the potentially transformative impact that lies at the interface of synthetic biology and biomaterials, the two fields have, so far, progressed mostly separately. This Perspective provides a review of recent key advances in these two fields, and a roadmap for collaboration at the interface between the two communities. We highlight the near-term applications of this interface to the development of hierarchically structured biomaterials, from bioinspired building blocks to ‘living’ materials that sense and respond based on the reciprocal interactions between materials and embedded cells. This Perspective reviews the complementary developments in synthetic biology and biomaterials and discusses how convergence of these two fields creates a promising design strategy for the fabrication of tailored living materials for medicine and biotechnology. 

/pdf/the-living-interface-between-synthetic-biology-and-3c0rty0m.pdf

The living interface between synthetic biology and biomaterial design

The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research. 

/pdf/advances-in-computational-intelligence-of-polymer-composite-crkooqxw.pdf

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

Learning physical descriptors for materials science by compressed sensing

Machine learning in materials genome initiative: A review

The data-driven model is the core issue of the Material Genome Initiative (MGI), but how to quickly obtain a large amount of material data has become a critical issue that needs to be resolved. At present, the sharing of material databases is low, so it is not easy to obtain useful material data from public resources. Therefore, we use the text mining method to obtain valid data from the literature of hypereutectic Al-Si alloy. Natural language processing (NLP) is a commonly used text mining method. Named entity recognition (NER), as one of the main tasks of NLP, can effectively extract information from the literature. However, there is no public dataset suitable for material entities recognition (MER) research in the material field. To effectively apply named entity recognition to the material field, five types of entities are selected from the material literature in this paper, and the hypereutectic Al-Si alloy material entity dataset (HASE) is constructed by manual annotation, which includes 8,845 material entities in total. At the same time, in the field of materials with only a small amount of annotation data, the MER method combined with active learning is proposed. Combined with the characteristics of the material entity, active learning adopts automatic annotation based on dictionary and rules, CRF model, and BiGRU-CRF model. In the end, a total of 16,677 material entities were annotated. The method of combining active learning not only improves the performance of the MER model but also reduces the cost of annotation. This method can more accurately extract effective material data in the literature. This research result provides an effective way for MGI researchers to quickly obtain a large amount of material data, which has theoretical significance and practical application value.

Text mining of hypereutectic Al-Si alloys literature based on active learning

The invention relates to a material performance prediction model construction method based on heterogeneous integrated learning, and belongs to the technical field of intersection of a computer and material science. A material performance prediction model is constructed through a Stacking method of a heterogeneous integrated learning method in machine learning, certain data guidance is provided for material design and manufacturing, the efficiency of new material research and development is improved, and the research and development period and the research and development cost are reduced. Compared with the prior art, the method mainly solves the problems that single-model prediction in existing material genetic engineering is low in accuracy; A plurality of different machine learning models are integrated into a strong prediction model (Stacking model) with the accuracy higher than that of a base learner through a Stacking ensemble learning method, and then the performance of the material is predicted through the model. The method has the effects that the prediction accuracy is greatly improved, the generalization ability of the model is improved, and reduction of the research anddevelopment cost of new materials is facilitated.

A material performance prediction model construction method based on heterogeneous integrated learning

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Papers

Machine learning in materials genome initiative: A review

Text mining of hypereutectic Al-Si alloys literature based on active learning

A material performance prediction model construction method based on heterogeneous integrated learning