Chapter 18: Autonomous Science: Big Data Tools for Small Data Problems in Chemistry
Published:15 Jul 2020
A. C. Geiger, Z. Cao, Z. Song, J. R. W. Ulcickas, and G. J. Simpson, in Machine Learning in Chemistry
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Machine learning tools are emerging to support autonomous science, in which critical decision-making on experimental design is conducted by algorithms rather than by human intervention. This shift from automation to autonomation is enabled by rapid advances in data science and deep neural networks, which provide new strategies for mining the ever-increasing volumes of data produced by modern instrumentation. However, a large number of measurements are intrinsically incompatible with high-throughput analyses, limited by time, the availability of materials, or the measurement architecture itself. Counter-intuitively, strategies developed for big-data challenges have the potential for major impacts in such data-limited problems. Two strategies for leveraging “big data” tools for small data challenges form the central theme of this chapter. In the first, advances in autonomous design of experiments are reviewed, in which algorithms select in real-time the next most informative experiments to perform based on results from previous measurements. Autonomous science enables maximization of confidence in scientific decision-making while simultaneously minimizing the number of measurements required to achieve that confidence. In the second, recent advances in adversarial strategies are reviewed for improving chemical decision-making with limited data. Adversarial attacks can help identify weak-points in classification and dimension reduction approaches that naturally arise in data-sparse training. Once identified, generative adversarial approaches provide a framework for “shoring up” those weak points by optimally leveraging the underlying probability distributions describing the input data. These illustrative examples highlight the rapidly evolving landscape of chemical measurement science enabled by machine learning.