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).

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)]. The release rate can be adjusted by 32-fold [rhodamine from MIL-101(Fe)-(NH)], 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)-(CH)(NH)], 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.

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.

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.

As molecular modeling and simulation techniques become increasingly important sources of thermophysical property and phase equilibrium data, the ability to assess the robustness of that data becomes more critical. Recently, the use of the compressibility factor (Z) has been suggested as a metric for testing the quality of simulation data for vapor− liquid equilibria (VLE). Here, we analyze predicted VLE data from the transferable potentials for phase equilibria (TraPPE) database and show that, apart from data entry or typographical errors, Z will always be well-behaved in Gibbs ensemble Monte Carlo (GEMC) simulations even when the simulations are not sufficiently equilibrated. However, this is not true for grand canonical Monte Carlo simulations. When the pressure is calculated from the internal forces, then pressure and density are strongly correlated for the vapor phase and, for GEMC simulations, it is recommended to treat Z as an instantaneous mechanical property. From analysis of the TraPPE VLE data, we propose a complementary metric based on the predicted vapor pressures at three neighboring temperatures and their deviation from a local Clausius−Clapeyron fit.

Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal–organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over three million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 286 zeolites, textural properties like pore sizes and surface areas, and the structure file for each material. We include metadata about the Monte Carlo simulations to enable reproducibility. The database is searchable by MOF properties, and the data are stored in a standardized JavaScript Object Notation format that is interoperable with the NIST adsorption database. We also identify several MOFs that meet high performance targets for multiple applications, such as high storage capacity for both hydrogen and methane or high CO2 capacity plus good Xe/Kr selectivity. By providing this data publicly, we hope to facilitate machine learning studies on these materials, leading to new insights on adsorption in MOFs and zeolites.

No abstract

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.