Machine intelligence supports the full chain of 2D dendrite synthesis

2026-03-17

AI Summary

This work addresses the challenges in optimizing and customizing two-dimensional ReSe₂ dendrite synthesis via chemical vapor deposition, where high-dimensional parameter spaces, scarce experimental data, and unclear growth mechanisms hinder progress. To overcome these limitations, we propose a machine intelligence–driven end-to-end synthesis framework that uniquely integrates active learning, accuracy-guided data augmentation, and a hybrid knowledge–data modeling strategy. By combining tree-based machine learning, interpretable models, and multiscale feature representation—augmented with thermodynamic and kinetic priors—our approach achieves efficient optimization of highly active ReSe₂ using only 60 experiments (<1.3% of the full parameter space). Furthermore, through nine additional targeted experiments, we precisely tune the fractal dimension to enable on-demand synthesis of user-specified morphologies and uncover multiscale cooperative mechanisms underlying multiparameter control.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• 2D dendrite synthesis
• parameter-intensive
• data-scarce
• reaction complexity
• fractal dimension control

Innovation

• machine intelligence
• active learning
• data augmentation
• interpretable ML
• fractal dimension