Nonlinear Dimensionality Reduction and Bayesian Optimization Tested for Biobased Foam Formulation Optimization

A scientific article titled 'Nonlinear dimensionality reduction and Bayesian optimization for accelerating design of materials' was published on 09 May 2026. com reported. The study evaluates nonlinear dimensionality reduction methods t-SNE and UMAP in comparison with PCA for biobased foam optimization.

It used an existing dataset comprising 26 distinct methylcellulose-cellulose fiber foam formulations with rheological and mechanical measurements. Optimizing biobased foam formulations is challenging because experiments are costly and fast-to-measure surrogate properties occupy high-dimensional spaces.

Bayesian optimization with Gaussian process regression is used to guide data-efficient searches for biobased foam formulations.

A Gaussian process was trained on low-dimensional representations of rheological observables from the 26-formulation dataset. Bayesian optimization was applied in a single-step, non-sequential manner on the fixed dataset to identify high-yield-stress regions. A second Gaussian process maps foam-formulation compositions to the reduced rheological coordinates.

Across all dimensionality reduction methods, Bayesian optimization identifies similar high-performing formulations achieving yield stress values comparable to the experimentally validated optimum. PCA is used as a baseline due to its deterministic and hyperparameter-free nature. Nonlinear methods (t-SNE and UMAP) can achieve comparable performance to PCA when appropriately tuned.

The study demonstrates that nonlinear DR-assisted BO provides a data-efficient framework for optimizing rheology-governed soft-matter materials. The keywords are: Dimensionality reduction (DR), Principal component analysis (PCA), T-distributed stochastic neighbor embedding (t-SNE), Uniform manifold approximation and projection (UMAP), Gaussian process (GP), Bayesian optimization (BO).