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1. A snapshot review on soft materials assembly design utilizing machine learning methods

   https://doi.org/10.1557/s43580-024-00852-x  

   Martirossyan, Maya M; Du, Hongjin; Dshemuchadse, Julia; Du, Chrisy Xiyu (May 2024, MRS Advances)

   Since the surge of data in materials-science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular-dynamics engines such as LAMMPS and HOOMD-blue. 

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2. Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis

   https://doi.org/10.1177/16094069251338898  

   Anakok, Isil; Katz, Andrew; Chew, Kai Jun; Matusovich, Holly (April 2025, International Journal of Qualitative Methods)

   We explore the possibility of using natural language processing (NLP) and generative artificial intelligence (GAI) to streamline the process of thematic analysis (TA) for qualitative research. We followed traditional TA phases to demonstrate areas of alignment and discordance between (a) steps one might take with NLP and GAI and (b) traditional thematic analysis. Using a case study, we illustrate the application of this workflow to a real-world dataset. We start with processes involved in data analysis and translate those into analogous steps in a workflow that uses NLP and GAI. We then discuss the potential benefits and limitations of these NLP and GAI techniques, highlighting points of convergence and divergence with thematic analysis. Then, we highlight the importance of the central role of researchers during the process of NLP and GAI-assisted thematic analysis. Finally, we conclude with a discussion of the implications of this approach for qualitative research and suggestions for future work. Researchers who are interested in AI-assisted methods can benefit from the roadmap we provide in this study to understand the current landscape of NLP and GAI models for qualitative research. 

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3. Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

   https://doi.org/10.1016/j.patter.2023.100863  

   Qian, Xiaoning; Yoon, Byung-Jun; Arróyave, Raymundo; Qian, Xiaofeng; Dougherty, Edward R. (November 2023, Patterns)

   Thanks to the rapid advances in artificial intelligence, AI for science (AI4Science) has emerged as one of the new promising research directions for modern science and engineering. In this review, we focus on recent efforts to develop knowledge-driven Bayesian learning and experimental design methods for accelerating the discovery of novel functional materials as well as enhancing the understanding of composition-process-structure-property relationships. We specifically discuss the challenges and opportunities in integrating prior scientific knowledge and physics principles with AI and machine learning (ML) models for accelerating materials and knowledge discovery. The current state-of-the-art methods in knowledge-based prior construction, model fusion, uncertainty quantification, optimal experimental design, and symbolic regression are detailed in the review, along with several detailed case studies and results in materials discovery. 

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4. Evolution of artificial intelligence for application in contemporary materials science

   https://doi.org/10.1557/s43579-023-00433-3  

   Gupta, Vishu; Liao, Wei-keng; Choudhary, Alok; Agrawal, Ankit (August 2023, MRS Communications)

   Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract 

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5. First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning

   https://doi.org/10.1038/s41524-021-00562-0  

   Pimachev, Artem K.; Neogi, Sanghamitra (June 2021, npj Computational Materials)

   Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes. 

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