Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
This work addresses the problem of time-consuming and inefficient materials synthesis for researchers and engineers, though it is incremental as it applies existing ML methods to a new domain-specific data type.
The researchers tackled the slow and manual process of optimizing nanomaterials synthesis by automating feature extraction from RHEED data using machine learning, achieving an 80% time saving in a 100-sample campaign and enabling predictions of grain alignment and dopant concentration from small expert-labeled datasets.
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.