Solvation dynamics of N-substituted acrylamide polymers and the importance for phase transition behavior.

Functional groups present in thermo-responsive polymers are known to play an important role in aqueous solutions by manifesting their coil-to-globule conformational transition in a specific temperature range. Understanding the role of these functional groups and their interactions with water is of great interest as it may allow us to control both the nature and temperature of this coil-to-globule transition. In this work, polyacrylamide (PAAm), poly(N-isopropylacrylamide) (PNIPAm), and poly(N-isopropylmethacrylamide) (PNIPMAm) solvated in water are studied with the goal of discovering the structure of the solvent and its interaction with these polymers in determining the polymer conformations. Specifically, all-atom molecular dynamics (MD) simulations were performed on polymer chains with 30 monomer units (30-mers) at 295 K, 310 K and 320 K, which is below and above the lower critical solution temperature (LCST) of PNIPAm (LCST = 305 K) and PNIPMAm (LCST = 315 K), respectively. The MD simulation trajectories suggest that changes in the functional groups in the backbone and side-chains alter the water solvation shell around the polymer. This results in a change in the residence time probability and hydrogen bond characteristics of water at simulated temperatures. Specifically, water molecules reside for longer times near PAAm (no LCST) and PNIPMAm (LCST = 315 K) chains as compared to PNIPAm. This might be one of the possible causes for the higher LCST of PNIPMAm as compared to that of PNIPAm. These results can guide experimentalists and theoreticians to design new polymer structures with tailor-made LCST transitions while controlling the water solvation shell around the functional group.

Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations.

Accurate, faster, and on-the-fly analysis of the molecular dynamics (MD) simulations trajectory becomes very critical during the discovery of new materials or while developing force-field parameters due to automated nature of these processes. Here to overcome the drawbacks of algorithm based analysis approaches, we have developed and utilized an approach that integrates machine-learning (ML) based stacked ensemble model (SEM) with MD simulations, for the first time. As a proof-of-concept, two SEMs were developed to analyze two dynamical properties of a water droplet, its contact angle, and hydrogen bonds. The two SEMs consisted of two layered networks of random forest, artificial neural network, support vector regression, Kernel ridge regression, and k-nearest neighbors ML models. The root-mean-square error values, uncertainty quantification, and sensitivity analysis of both the SEMs suggested that the final result was more accurate as compared to that of the individual ML models. This new computational framework is very general, robust, and has a huge potential in analyzing large size MD simulation trajectories as it can capture critical information very accurately.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios.

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (Mpro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and Mpro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.