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, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. Employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

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 4.5 × 109 molecules for the TIP4P/2005 and mW water models, respectively. The computationally more efficient mW water model allows us to reach converging behavior when the bubble dynamics results are plotted in reduced units, and the limiting behavior can be obtained through linear extrapolation in L-1. Qualitative differences are observed between simulations with the mW and TIP4P/2005 water models, but they can be explained by the models' differences in predicted viscosity and surface tension. Although bubble collapse occurs on time scales of only hundreds of picoseconds, the system sizes used here are sufficiently large to obtain bubble dynamics consistent with the Rayleigh-Plesset equation when using the models' thermophysical properties as input. For the conditions explored here, extreme heating of the interfacial water molecules near the time of collapse is observed for the larger mW water systems (but the model underpredicts the viscosity), whereas heating is less pronounced for the TIP4P/2005 water systems because its larger viscosity contribution slows the collapse dynamics. The presence of nitrogen within the bubble only starts to affect bubble dynamics near the very end of the initial collapse, leading to an incomplete collapse and strong rebound for the mW water model. Although nitrogen is non-condensable at 300 K, it becomes highly compressed and reaches a liquid-like density near the collapse point. We find that the dissolution of nitrogen is much slower than the movement of the collapsing water front, and the re-expansion of the dense nitrogen droplet gives rise to bubble rebound. The incompatibility of the collapse and dissolution time scales should be considered for continuum-scale modeling of bubble dynamics. We also confirm that the diffusion coefficient for dissolved nitrogen is insensitive to pressure as the liquid transitions from a compressed to a stretched state.

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. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

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. Capturing 3-dimensional periodic structures, such as ordered network morphologies, is hampered by the requirement that the number of molecules in the simulated system and the shape of the periodic simulation box need to be commensurate with those of the resulting network phase. Common strategies for structure identification include structure factors and order parameters, but these fail to identify imperfect structures in simulations with incorrect system sizes. Building upon pioneering work by DeFever et al. [Chem. Sci. 2019, 10, 7503-7515] who implemented a PointNet (i.e., a neural network designed for computer vision applications using point clouds) to detect local structure in simulations of single-bead particles and water molecules, we present a PointNet for detection of nonlocal ordered morphologies of complex block oligomers. Our PointNet was trained using atomic coordinates from molecular dynamics simulation trajectories and synthetic point clouds for ordered network morphologies that were absent from previous simulations. In contrast to prior work on simple molecules, we observe that large point clouds with 1000 or more points are needed for the more complex block oligomers. The trained PointNet model achieves an accuracy as high as 0.99 for globally ordered morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star-block oligomers, and it also allows for the discovery of emerging ordered patterns from nonequilibrium systems.

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 (N 1 N 2 VT) 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. The NN accurately reproduces simulation results and is able to obtain a continuous isotherm function. Its predictions can be therefore utilized to facilitate optimization of desorption conditions, which requires a laborious iterative search if undertaken by simulation alone. Furthermore, it learns information about the binary sorption equilibria as hidden layer representations. This allows for application of transfer learning with limited data by fine-tuning a pretrained NN for a different alkanediol/solvent/zeolite system.

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 2.2 to 4.2 nm to precisely include single-stranded DNA (ssDNA, 11-53 nt), and to achieve reversible interaction between MOFs and ssDNA. The entire nucleic acid chain is completely confined inside the pores providing excellent protection, and the geometric distribution of the confined ssDNA is visualized by X-ray diffraction. Two MOFs in this series exhibit excellent transfection efficiency in mammalian immune cells, 92% in the primary mouse immune cells (CD4+ T cell) and 30% in human immune cells (THP-1 cell), unrivaled by the commercialized agents (Lipo and Neofect).

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)-(NH2)x, MIL-101(Fe)-(C4H4)x, and MIL-101(Fe)-(C4H4)x(NH2)1-x]. The release rate can be adjusted by 32-fold [rhodamine from MIL-101(Fe)-(NH2)x], and the time of release peak can be shifted by up to 12 days over a 40-day release period [doxorubicin from MIL-101(Fe)-(C4H4)x(NH2)1-x], which was not obtained in the physical mixture of the single component MOF counterparts nor in other porous materials. The corelease of two pro-drug molecules (ibuprofen and doxorubicin) was also achieved.