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. The 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.

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Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab initio Calculations. -(HAUTIER, G.; JAIN, A.; ONG, S. P.; KANG, B.; MOORE, C.; DOE, R.; CEDER*, G.; Chem. Mater. 23 (2011) 15, 3495-3508, http://dx.doi.org/10.1021/cm200949v ; Dep. Mater. Sci. Eng., MIT, Cambridge, MA 02139, USA; Eng.) -Schramke 37-008

There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis data sets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies (Δ G f , Δ H f ). In contrast, features derived from the thermodynamics of synthesis-related reactions did not directly correlate to the chosen heating temperatures. This correlation between optimal solid-state heating temperature and precursor stability extends Tamman’s rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in determining synthesis conditions. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of human bias in the data set. Using these predictive features, we constructed machine-learning models with good performance and general applicability to predict the conditions required to synthesize diverse chemical systems.

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Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further, with advancements in Generative Adversarial Networks (GANs), now changing attributes and retouching have become very easy. Such synthetic alterations have adverse effect on face recognition algorithms. While researchers have proposed to detect image tampering, detecting GANs generated images has still not been explored. This paper proposes a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images. The algorithm yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset. It outperforms the previous state of the art which reported an accuracy of 87% on the database. For distinguishing between real images and images generated using GANs, the proposed algorithm yields an accuracy of 99.83%.

After nearly seven decades of development, dental composite restorations continue to show limited clinical service. The triggering point for restoration failure is the degradation of the bond at the tooth–biomaterial interface from chemical, biological, and mechanical sources. Oral biofilms form at the bonded interfaces, producing enzymes and acids that demineralize hard tissues and damage the composite. Removing bacteria from bonded interfaces and remineralizing marginal gaps will increase restorations’ clinical service. To address this need, we propose for the first time the use of piezoelectric nanoparticles of barium titanate (BaTiO3) as a multifunctional bioactive filler in dental resin composites, offering combined antibacterial and (re)­mineralization effects. In this work, we developed and characterized the properties of dental piezoelectric resin composites, including the degree of conversion and mechanical and physical properties, for restorative applications. Moreover, we evaluated the antibacterial and mineralization responses of piezoelectric composites in vitro. We observed a significant reduction in biofilm growth (up to 90%) and the formation of thick and dense layers of calcium phosphate minerals in piezoelectric composites compared to control groups. The antibacterial mechanism was also revealed. Additionally, we developed a unique approach evaluating the bond strength of dentin–adhesive–composite interfaces subjected to simultaneous attacks from bacteria and cyclic mechanical loading operating in synergy. Our innovative bioactive multifunctional composite provides an ideal technology for restorative applications using a single filler with combined long-lasting nonrechargeable antibacterial/remineralization effects.