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| 1 | A general-purpose machine learning framework for predicting properties of inorganic materials显示文摘A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability. | Logan Ward Ankit Agrawal Alok Choudhary Christopher Wolverton | 2016 | npj Computational Materials2016，，1： | 83 |
| 2 | The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design显示文摘The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment. | Kamal Choudhary Kevin F.Garrity Andrew C.E.Reid Brian DeCost Adam J.Biacchi Angela R.Hight Walker Zachary Trautt Jason Hattrick-Simpers A.Gilad Kusne Andrea Centrone Albert Davydov Jie Jiang Ruth Pachter Gowoon Cheon Evan Reed Ankit Agrawal Xiaofeng Qian Vinit Sharma Houlong Zhuang Sergei V.Kalinin Bobby G.Sumpter Ghanshyam Pilania Pinar Acar Subhasish Mandal Kristjan Haule David Vanderbilt Karin Rabe Francesca Tavazza | 2020 | npj Computational Materials2020，，1： | 10 |
| 3 | Recent advances and applications of deep learning methods in materials science显示文摘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. | Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton | 2022 | npj Computational Materials2022，，1： | 9 |
| 4 | An adaptive fuzzy thresholding algorithm for exon prediction显示文摘 | Ankit Agrawal Ankush Mittal Rahul Jain Raghav Takkar | 2008 | IEEE ICCCET2008，，： | 1 |
| 5 | A kinetic study ofpyrolysis and combustion of microalgae Chlorella vul-garis using thermo-gravimetric analysis 显示文摘 | Agrawal Ankit Chakraborty Saikat | 2013 | BioresourceTechnology2013，128，： | 1 |
| 6 | An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems显示文摘Materials design aims to identify the material features that provide optimal properties for various engineering applications,such as aerospace,automotive,and naval.One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties.This paper proposes an end-to-end artificial intelligence(AI)-driven microstructure optimization framework for elastic properties of materials.In this work,the microstructure is represented by the Orientation Distribution Function(ODF)that determines the volume densities of crystallographic orientations.The framework was evaluated on two crystal systems,cubic and hexagonal,for Titanium(Ti)in Joint Automated Repository for Various Integrated Simulations(JARVIS)database and is expected to be widely applicable for materials with multiple crystal systems.The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time. | Yuwei Mao Mahmudul Hasan Arindam Paul Vishu Gupta Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Pinar Acar Ankit Agrawal | 2023 | npj Computational Materials2023，，1： | 0 |
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