Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization

TL;DR

This paper introduces machine learning-based feature extraction from RHEED data to predict and accelerate nanomaterials synthesis, achieving significant time savings and improved process control.

Contribution

It develops a general machine learning approach for RHEED data analysis that predicts film properties and dopant levels without system-specific retraining.

Findings

Achieved up to 80% time savings in synthesis campaigns

Successfully predicted grain alignment from substrate data

Estimated dopant concentration using in-situ RHEED

Abstract

Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis.