Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials.

Using knowledge from statistical thermodynamics and crystallography, we develop an image-image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate-adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, -butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K.

Large-scale molecular dynamics simulations of bubble collapse in water: Effects of system size, water model, and nitrogen.

Molecular dynamics simulations in the microcanonical ensemble are performed to study the collapse of a bubble in liquid water using the single-site mW and the four-site TIP4P/2005 water models. To study system size effects, simulations for pure water systems are performed using periodically replicated simulation boxes with linear dimensions, L, ranging from 32 to 512 nm with the largest systems containing 8.7 × 106 and 4.

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning.

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference.

Development of a PointNet for Detecting Morphologies of Self-Assembled Block Oligomers in Atomistic Simulations.

Molecular simulations with atomistic or coarse-grained force fields are a powerful approach for understanding and predicting the self-assembly phase behavior of complex molecules. Amphiphiles, block oligomers, and block polymers can form mesophases with different ordered morphologies describing the spatial distribution of the blocks, but entirely amorphous nature for local packing and chain conformation. Screening block oligomer chemistry and architecture through molecular simulations to find promising candidates for functional materials is aided by effective and straightforward morphology identification techniques.

Deep neural network learning of complex binary sorption equilibria from molecular simulation data.

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical ( ) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying process for (1,4-butanediol or 1,5-pentanediol)/water and 1,5-pentanediol/ethanol from all-silica MFI zeolite and 1,5-pentanediol/water from all-silica LTA zeolite. A multi-task deep NN was trained on the simulation data to predict equilibrium loadings as a function of thermodynamic state variables.

Metal-organic frameworks for precise inclusion of single-stranded DNA and transfection in immune cells.

Effective transfection of genetic molecules such as DNA usually relies on vectors that can reversibly uptake and release these molecules, and protect them from digestion by nuclease. Non-viral vectors meeting these requirements are rare due to the lack of specific interactions with DNA. Here, we design a series of four isoreticular metal-organic frameworks (Ni-IRMOF-74-II to -V) with progressively tuned pore size from 2.

Multivariate Metal-Organic Frameworks for Dialing-in the Binding and Programming the Release of Drug Molecules.

We report the control of guest release profiles by dialing-in desirable interactions between guest molecules and pores in metal-organic frameworks (MOFs). The interactions can be derived by the rate constants that were quantitatively correlated with the type of functional group and its proportion in the porous structure; thus the release of guest molecules can be predicted and programmed. Specifically, three probe molecules (ibuprofen, rhodamine B, and doxorubicin) were studied in a series of robust and mesoporous MOFs with multiple functional groups [MIL-101(Fe)-(NH), MIL-101(Fe)-(CH), and MIL-101(Fe)-(CH)(NH)].