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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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Evolution of artificial intelligence for application in contemporary materials science
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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- Award ID(s):
- 2053929
- PAR ID:
- 10441905
- Publisher / Repository:
- Cambridge University Press (CUP)
- Date Published:
- Journal Name:
- MRS Communications
- Volume:
- 13
- Issue:
- 5
- ISSN:
- 2159-6867
- Format(s):
- Medium: X Size: p. 754-763
- Size(s):
- p. 754-763
- Sponsoring Org:
- National Science Foundation
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