Alexandra L Day, Carolin B Wahl, Vishu Gupta, Roberto Dos Reis, Wei-Keng Liao, Chad A Mirkin, Vinayak P Dravid, Alok Choudhary, Ankit Agrawal
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引用次数: 0
摘要
传统上,材料发现更多地依靠证据和直觉,而非系统设计。然而,"大数据 "的出现和计算能力的指数级增长重塑了这一格局。如今,我们利用模拟、人工智能(AI)和机器学习(ML)来预测材料特性,这大大加快了新型材料的发现。例如,在单个芯片上创建数百万个不同纳米粒子的组合巨库,激发了对自动表征工具的需求。本文介绍了一种 ML 模型,该模型专门用于对来自这些巨型库的纳米颗粒的灰度高角度环形暗场图像进行实时二元分类。鉴于与下游处理错误相关的高成本,我们模型的主要要求是在保持对未见图像的有效性的同时,最大限度地减少误报。我们详细阐述了计算方面的挑战和我们的解决方案,包括管理内存限制、优化训练时间和利用神经架构搜索工具。最终的模型超出了我们的预期,在测试数据集上达到了 95% 以上的精确度和 90% 以上的加权 F 分数。本文讨论了人工智能和 ML 在材料发现领域应用的这一重大进展的开发、挑战和成功结果。
Machine Learning-Enabled Image Classification for Automated Electron Microscopy.
Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.
期刊介绍:
Microscopy and Microanalysis publishes original research papers in the fields of microscopy, imaging, and compositional analysis. This distinguished international forum is intended for microscopists in both biology and materials science. The journal provides significant articles that describe new and existing techniques and instrumentation, as well as the applications of these to the imaging and analysis of microstructure. Microscopy and Microanalysis also includes review articles, letters to the editor, and book reviews.