It is easy to recognize the benefits of the machine-learning approach to, for example, testing chemicals and materials for toxicity -- an area that we work on as a combined team of computer scientists and chemists. First, the need is obvious when you consider that less than 1% of the chemicals registered for commercial use in the United States have undergone toxicity characterization, whether they are used for medicinal purposes or for fracking. Moreover, there are many scientific, ethical, and economic advantages to replacing the animals currently used in toxicity tests with non-animal test systems, and great speed and cost advantages in using computer systems. Second, material and chemical usage has increased to 60 billion tonnes per year during the twentieth century2, underscoring the advantages of a rapid machine-learning approach for toxicity characterization. Finally, the number of materials and chemicals that can be designed digitally far exceeds the number that have been well characterized. For example, our estimates based on the number of material combinations with six surfaces exceed trillions, while the organic chemicals based on only hexanes exceed 1030 (Figure 1), clearly indicating the vastness of possibilities.