Machine intelligence supports the full chain of 2D dendrite synthesis

TL;DR

This paper presents a machine intelligence framework that enhances the entire process of 2D dendrite synthesis, including optimization, customization, and understanding of mechanisms, with minimal experiments.

Contribution

It introduces an integrated machine learning approach combining active learning, data augmentation, and interpretable models for efficient and insightful material synthesis.

Findings

Optimized dendrite growth with fewer experiments.

Unveiled nonlinear relationships between process variables and morphology.

Guided the synthesis of dendrites with desired fractal dimensions.

Abstract

Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis.