Utilization of machine learning to accelerate colloidal synthesis and discovery.

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

doi:10.1063/5.0047385

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.

Highlights

The discovery of novel materials has potential to solve a myriad of grand challenges, ranging from the world’s rapidly changing climate to humanity’s need for water and food security
Experimental materials discovery typically requires four general procedures: control over the synthesis conditions, measurement, analysis of the outcome, and planning the best experiment. This is exemplified in the laboratory where a person selects the starting chemicals and experimental parameters, synthesizes the products in a Schlenk line reactor, extracts the products and prepares them for measurement [usually transmission electron microscopy (TEM)], analyzes the outcome in relation to the synthesis conditions, and plans the best experiment by intuition
The finished nanoparticles are probed in situ by small-angle x-ray scattering (SAXS) at the output, and the scattering pattern is analyzed to estimate the statistics of the size distribution

Summary

Introduction

The discovery of novel materials has potential to solve a myriad of grand challenges, ranging from the world’s rapidly changing climate to humanity’s need for water and food security. These challenges are significant that the solutions require technological advancements beyond the current state of the art and because these solutions need to be developed and deployed much more rapidly than has occurred in recent history. Accelerating the discovery-to-deployment timeline with machine learning techniques is not a novel concept Efforts such as AFLOW, Materials Project, and the Open Quantum Materials Database have demonstrated success in accelerating the rate of discovery using computational design of materials on massive scales. These resources utilize high-performance computing to predict materials properties across hundreds of thousands of candidate materials in order to identify materials, which are most likely to meet the design targets for the intended application.

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Conclusion