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Electrostatic configurations─the spatial arrangement of charged sites within an adsorbent─can profoundly influence the adsorbent’s interaction with water and the resulting cluster formation and their orientation. This design feature can serve as a tuning parameter for water vapor adsorption to achieve the desired isotherm behavior. Hence, understanding the role of electrostatic configurations in water vapor adsorption can inform many established and emerging areas concerning the water-energy nexus and water security. In this work, we apply continuous fractional component grand canonical Monte Carlo (CFC-GCMC) to perform water adsorption simulations in idealized cylindrical nanopores across five different charge configurations with varying pore sizes (1, 1.1, and 1.2 nm) and charge magnitudes (∼±0.39–1.17). The alternating along (AA) configuration (positive charges in the inner ring and negative charges in the outer ring while alternating in the z-direction) demonstrates higher water uptake at saturation, and water adsorption starts at a much lower pressure than other configurations. Analysis of the water clustering pattern in AA reveals both radial and axial expansions of water clusters, which facilitates accommodation of extra water molecules. Increasing the charge magnitude shifts the type-V isotherm inflection point to lower pressure, thereby increasing the hydrophilic nature of the cylinder. Probing different energetic interactions and electrostatic potentials of the configuration suggests the unique relaxation of the water clusters in the AA patterned cylinders. Investigating the effect of charge magnitude and pore size provides more insight into their hydrophilic nature. Finally, analyzing the hydrogen bonding and adsorbed phase characteristics at saturation hints at strong ordering induced by pore confinements and electrostatic configurations compared with bulk liquid water. The simulations show that tailored charge arrangements can enhance adsorption by facilitating uptake at a lower pressure and achieving a higher water capacity at saturation. This study presents original insights into the interplay of electrostatic configuration, pore size, and charge strength in controlling water vapor adsorption within nanopores and the resulting confined water vapor structure. 

Enhanced drug testing efficiency has driven the prominence of high‐content and high‐throughput screening (HCHTS) in drug discovery and development. However, traditional HCHTS in well‐plates often lack complexity of in vivo conditions. 3D cell cultures, like cellular spheroids/organoids, offer a promising alternative by replicating in vivo conditions and improving the reliability of drug responses. Integrating spheroids/organoids into HCHTS requires strategies to ensure uniform formation, systemic function, and compatibility with analysis techniques. This study introduces an easy‐to‐fabricate, low‐cost, safe, and scalable approach to create a bioinert hydrogel‐based inverted colloidal crystal (BhiCC) framework for uniform and high‐yield spheroid cultivation. Highly uniform alginate microgels are fabricated and assembled into a colloidal crystal template with controllable contact area, creating engineered void spaces and interconnecting channels within agarose‐based BhiCC through the template degradation by alginate lyase and buffer. This results in a multi‐layered iCC domain, enabling the generation of in‐vitro 3D culture models with over 1000 spheroids per well in a 96‐well plate. The unique hexagonal‐close‐packed geometry of iCC structure enables HCHTS through conventional plate reader analysis and fluorescent microscopy assisted by house‐developed automated data processing algorithm. This advancement offers promising applications in tissue engineering, disease modeling, and drug development in biomedical research. 

In-situ characterization techniques, although complex, can provide a wealth of insight into material chemistry and evolution dynamics. Grasping the fundamental kinetics of material synthesis is essential to enhance and streamline these processes and facilitate easier scaleup. Metal–organic frameworks (MOFs), a class of porous crystalline materials discovered three decades ago, have been developed and implemented in various applications at the laboratory scale. However, only a few studies have explored the fundamental mechanisms of their formation that determine their physical structure and chemical properties. Independent experimental and theoretical investigations focusing on chemical kinetics have provided some understanding of the mechanisms governing MOF formation. However, more effort is needed to fully control their formation pathways and properties to enhance stability, optimize performance, and design strategies for scalable production. This Perspective highlights current techniques for studying MOF kinetics, discusses their limitations, and proposes multimodal experimental and theoretical protocols, emphasizing how improved data acquisition and multiscale approaches can advance scalable applications. 

Active learning facilitated adsorption predictions in porous materials by incorporating “alchemical” molecules, fugacity, and material features (PC1, PC2) using Gaussian process regression. 

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models. 

You will likely have heard of absorption before, but have you heard of adsorption? At the University of Notre Dame in Indiana, USA, Dr Yamil Colón is a chemical and biomolecular engineer studying this important chemical process. His work could help make huge breakthroughs in healthcare, climate change, environment and water scarcity research. 

In recent decades, metal–organic frameworks (MOFs) have gained recognition for their potential in multicomponent gas separations. Though molecular simulations have revealed structure–property relationships of MOF–adsorbate systems, they can be computationally expensive and there is a need for surrogate models that can predict the adsorption data faster. In this work, an active learning (AL) protocol is introduced that can predict multicomponent gas adsorption in a MOF for a range of thermodynamic conditions. This methodology is applied to build a model for the adsorption of three different gas mixtures (CO2–CH4, Xe–Kr, and H2S–CO2) in the MOF Cu-BTC. A Gaussian process regression (GPR) model is used to fit the data as well to leverage its predicted uncertainty to drive the learning. The training data is generated using grand-canonical Monte Carlo (GCMC) simulations as points are iteratively added to the model to minimize the predicted uncertainty. Also, a criteria which captures the perceived performance of the GPs is introduced to terminate the AL process when the perceived accuracy threshold is met. The three systems are tested for a pressure–mole fraction (P–X), and a pressure–mole fraction–temperature (P–X–T) feature space. It is demonstrated that AL one only needs a fraction of the data from simulations to build a reliable surrogate model for predicting mixture adsorption. Further, the final GP fit from AL outperforms ideal adsorbed solution theory predictions. 

High-throughput molecular simulations and machine learning (ML) have been implemented to adequately screen a large number of metal−organic frameworks (MOFs) for applications involving adsorption. Grand canonical Monte Carlo (GCMC) simulations have proven effective in calculating the adsorption capacity at given pressures and temperatures, but they can require expensive computational resources. While they can be resource-efficient, ML models can require large datasets, creating a need for algorithms that can efficiently characterize adsorption; active learning (AL) can play a very important role in this regard. In this work, we make use of Gaussian process regression (GPR) to model pure component adsorption of nitrogen at 77 K from 10−5 to 1 bar, methane at 298 K from 10 −5 to 100 bar, carbon dioxide at 298 K from 10−5 to 100 bar, and hydrogen at 77 K from 10−5 to 100 bar on PCN-61, MgMOF-74, DUT-32, DUT-49, MOF-177, NU-800, UiO-66, ZIF-8, IRMOF-1, IRMOF-10, and IRMOF-16. The GPR model requires an initial training of the model with an initial dataset, the prior one, and, in this study of evaluating AL, we make use of three different prior selection schemes. Each prior scheme is updated with a sampling point resulting from the GP model uncertainties. This protocol continues until a maximum GPR relative error of 2% is attained. We make a recommendation on the best prior selection scheme for the total 44 adsorbate−adsorbent pairs primarily making use of the mean absolute error and the total amount of points required for convergence of the model. To further evaluate the AL framework, we apply the BET consistency criteria on the simulated and GP nitrogen isotherms and compare the resulting surface areas. 

Metal–organic frameworks (MOFs) are promising materials with various applications, and machine learning (ML) techniques can enable their design and understanding of structure–property relationships. In this paper, we use machine learning (ML) to cluster the MOFs using two different approaches. For the first set of clusters, we decompose the data using the textural properties and cluster the resulting components. We separately cluster the MOF space with respect to their topology. The feature data from each of the clusters were then fed into separate neural networks (NNs) for direct learning on an adsorption task (methane or hydrogen). The resulting NNs were then used in transfer learning (TL) where only the last NN layer was retrained. The results show significant differences in TL performance based on which cluster is chosen for direct learning. We find TL performance depends on the Euclidean distance in the decomposed feature space between the clusters involved in the direct and TL. Similar results were found when TL was performed simultaneously across both types of clusters and adsorption tasks. We note that methane adsorption was a better source task than hydrogen adsorption. Overall, the approach was able to identify MOFs with the most transferable information, leading to valuable insights and a more comprehensive understanding of the MOF landscape. This highlights the method's potential to generate a deeper understanding of complex systems and provides an opportunity for its application in alternative datasets. 

Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. ACS Applied Materials & Interfaces 2022, 14, 37161−37169.], ML models were applied to predict the adhesive free energy of polymer–surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive data sets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small data sets by pretraining on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pretrained model facilitates the prediction accuracy significantly on small data sets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design.