Utilization of machine learning to accelerate colloidal synthesis and discovery

Fong, Anthony Y.; Pellouchoud, Lenson; Davidson, Malcolm; Walroth, Richard C.; Church, Carena; Tcareva, Ekaterina; Wu, Liheng; Peterson, Kyle; Meredig, Bryce; Tassone, Christopher J.

doi:10.1063/5.0047385

DOE OSTI · 1784063

Utilization of machine learning to accelerate colloidal synthesis and discovery

Fong, Anthony Y.·Pellouchoud, Lenson·Davidson, Malcolm·Walroth, Richard C.·Church, Carena·Tcareva, Ekaterina·Wu, Liheng·Peterson, Kyle·Meredig, Bryce·Tassone, Christopher J.

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Abstract

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Gaussian processes informatics machine learning nanocrystals x-ray scattering

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Fong, Anthony Y., Pellouchoud, Lenson, Davidson, Malcolm, Walroth, Richard C., Church, Carena, Tcareva, Ekaterina, Wu, Liheng, Peterson, Kyle, Meredig, Bryce, Tassone, Christopher J.. 2021-06-14. Utilization of machine learning to accelerate colloidal synthesis and discovery. https://doi.org/10.1063/5.0047385

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