Engineering topics

Colón, Yamil J.

Publications and source records attributed to Colón, Yamil J..

3 records

DOE OSTI2024

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Expanding the Design Space of Polymer–Metal Organic Framework (MOF) Gels by Understanding Polymer–MOF Interactions

The fabrication of polymer-MOF composite gels holds great potential to provide emergent properties for drug delivery, environmental remediation, and catalysis. To leverage the full potential of these composites, we investigated how the presence and chemistry of polymers impact MOF formation within the composites and, in turn, how MOFs impact polymer gelation. We show that polymers with a high density of strongly metal-binding carboxylic acids inhibit MOF formation; however, reducing the density of carboxylic acids or substituting them with weaker metal-binding hydroxyl groups permits both MOF formation and gelation within composites. Preparing composites with poly(ethylene glycol) (PEG), which does not bind MOF zirconium (Zr)-oxo clusters, and observing gelation suggests that MOFs can entrap polymer chains to create cross-links in addition to cross-linking them through polymer-Zr-oxo interactions. Both simulations and experiments show composite hydrogels formed with poly(vinyl alcohol) (PVA) to be more stable than those made with PEG, which can reptate through MOF pores upon heating. We demonstrate the generalizability of this composite formation process across different Zr-based MOFs (UiO-66, NU-901, UiO-67, and MOF-525) and by spin-coating gels into conformable films. PVA-UiO-66 composite hydrogels demonstrated high sorption and sustained release of methylene blue relative to the polymer alone (3× loading, 28× slower release), and PVA-MOF-525 composite hydrogels capably sorb the therapeutic peptide Angiotensin 1–7. By understanding the influence of polymer-MOF interactions on the structure and properties of composite gels, this work informs and expands the design space of this emerging class of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗