am9b14792_si_003.mp4 (729.55 kB)
Accelerated Design of Catalytic Water-Cleaning Nanomotors via Machine Learning
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posted on 2019-10-18, 17:34 authored by Minxiang Zeng, Shuai Yuan, Dali Huang, Zhengdong ChengThe ability of self-propelled nanoparticles
to convert environmental
energy into locomotion has led to several nanomotor prototypes that
are promising in numerous real-world applications. However, the vast
variety of nanoparticle designs prevents rapid identification of the
optimal composition for a given application. In this study, we applied
machine learning methods to predict the self-propulsion speed and
water-cleaning efficiency of micro/nanomotors (MNMs), where the quality
of machine learning predictions was evaluated based on the statistical
values. The average absolute error of predicted velocity and predicted
efficiency are determined to be as low as 0.10 and 0.12, respectively.
In addition, by comparing the prediction results based on 13 features
using four different machine learning algorithms, we are able to identify
several key features that are important to effectively environmental
decontamination, such as particle size, catalyst type, and aspect
ratio. Following the guidelines deduced from these models, a high-efficiency
Pt-coated nanomotor was designed and synthesized, of which the experimental
results were compared with the machine learning predictions, showing
an accurate prediction with a less than 15% of prediction error. In
the range of our theoretical/experimental conditions, we showed that
a gradient boosting algorithm is the most promising method for predicting
the environmental decontamination behavior of MNMs, a machine-learning
algorithm rarely used in the nanomaterial field in current practice.
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nanomaterial field13 featuresmachine-learning algorithmhigh-efficiency Pt-coated nanomotoraspect ratioapplicationMNMprediction errorprediction resultscatalyst typeparticle sizemethodwater-cleaning efficiencydecontamination behaviorMachine Learningself-propulsion speedCatalytic Water-Cleaning Nanomotorsnanoparticle designsself-propelled nanoparticlesnanomotor prototypes
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