Machine-Learning-Based Dispersion Optimizer for Carbon Nanotubes across Dispersant–Solvent–Process Space

Appropriately pairing dispersants
and solvents and tuning processing
conditions yield high-quality, stable carbon nanotube (CNT) dispersions;
however, experimental investigation of this combinatorial design space
is slow and nonsystematic. We report a machine-learning-based CNT
dispersion optimizer that simultaneously considers dispersant, solvent,
and the dispersion process to predict CNT dispersibility and crystallinity.
The data set comprised 666 dispersions (36 organic dispersants, 22
solvents, and two dispersion methods) with systematic variations in
composition and processing parameters. Dscore and IG/ID quantified the dispersion quality and structural integrity, respectively.
Using molecular descriptors, experimental variables, and solvent–dispersant
similarity metrics as inputs, an eXtreme Gradient Boosting (XGBoost)
model achieved a coefficient of determination (R2) = 0.57, mean absolute error (MAE) = 0.08 for Dscore and R2 = 0.73, MAE =
9.84 for IG/ID. These correspond to mean absolute errors below 10% of the target
ranges, indicating sufficient performance for screening-grade formulation
design. Limited quantitative accuracy was displayed for dispersants
outside the training set; solvent-dependent trends were reproduced,
and practically useful formulations were identified. Virtual screening
within the learned domain yielded improved formulations. SHapley Additive
exPlanations and feature-group ablation revealed that Dscore was governed primarily by solvent–dispersant
compatibility encoded by similarity and distance-like descriptors,
while IG/ID was dominated by process intensity. These elements constitute a
CNT dispersion optimizer that efficiently prescreens formulation and
processing conditions and can be extended to other nanomaterial dispersion
systems. This prescreening framework reduces empirical trial-and-error
and promotes solvent- and process-constrained formulation design by
treating dispersions as enabling intermediate materials for the downstream
manufacturing of films, fibers, and composites.