Molecular Systems Design & Engineering最新文献

Soft porous coordination polymers (SPCPs) are flexible porous materials comprised of metal–organic polyhedrons (MOPs) connected by organic linkers, with potential in adsorption applications. We performed molecular simulations of various SPCPs that vary in the length and flexibility of the organic linkers to address how the flexibility can result in various configurations and affects adsorption performance. We examined free energy profiles as a function of volume of different SPCPs while varying methane loading, resulting in different stable configurations. We found significant differences in the volume of the stable configurations and their number for the various structures, with more flexible linkers having more stable configurations in free energy. We also characterized the textural properties and methane adsorption isotherms of the stable configurations for the SPCPs and analyzed density profiles of the adsorption in the various configurations. Altogether, our examination can be used to predict the relevant configurations of the SPCPs at a given loading and provides molecular-level understanding of how the flexibility of the organic linkers affects the structure of the system and adsorption performance.

An efficient screening of azobenzene (AB) derivatives for Molecular Solar Thermal (MOST) applications based on ground state properties (energy stored per molecule and Z isomer stability) could be performed with quasi-CASPT2 accuracy. In this work, we show how wavefunction and electron density based methods can be efficiently combined in a computational protocol that yields accurate potential energy profiles with a significant reduction in computational cost compared to that of a fully-CASPT2 characterization. Our results on prototypical electron donor/withdrawing AB derivatives clearly identify pull–pull substitution as the most promising, allowing to draw guidelines for the chemical design of promising azo-MOST candidates.

Peptides are a powerful class of molecules that can be applied to a range of problems including biomaterials development and drug design. Currently, machine learning-based property prediction models for peptides primarily rely on amino acid sequence, resulting in two key limitations: first, they are not compatible with non-natural peptide features like modified sidechains or staples, and second, they use human-crafted features to describe the relationships between different amino acids, which reduces the model's flexibility and generalizability. To address these challenges, we have developed PepMNet, a deep learning model that integrates atom-level and amino acid-level information through a hierarchical graph approach. The model first learns from an atom-level graph and then generates amino acid representations based on the atomic information captured in the first stage. These amino acid representations are then combined using graph convolutions on an amino acid-level graph to produce a molecular-level representation, which is then passed to a fully connected neural network for property prediction. We evaluated this architecture by predicting two peptide properties: chromatographic retention time (RT) as a regression task and antimicrobial peptide (AMP) activity as a classification task. For the regression task, PepMNet achieved an average R² of 0.980 across eight datasets, which spanned different dataset sizes and three liquid chromatography (LC) methods. For the classification task, we developed an ensemble of five models to reduce overfitting and ensure robust classification performance, achieving an area under the receiver operating curve (AUC-ROC) of 0.978 and an average precision of 0.981. Overall, our model illustrates the potential for hierarchical deep learning models to learn peptide properties without relying on human engineering amino acid features.

Two-photon 3D laser printing (2PLP) is one of the most versatile methods for additive manufacturing of micro- to nano-scale objects with arbitrary geometries and fine features. With advancing technological capability and accessibility, the demand for new and versatile inks is increasing, with a trend toward printing functional or responsive structures. One approach for ink design is the use of a macromolecular ink consisting of a ‘pre-polymer’ functionalized with photocrosslinkable groups to enable printability. However, so far the synthesis of pre-polymer inks for 2PLP often relies on an arbitrary choice rather than systematic design. Additionally, current structure–property relationship studies are limited to commercial or small molecule-based inks. Herein, three macromolecular inks with varied compositions, molecular weights, and glass transition temperatures are synthesized and formulated into inks for 2PLP. 3D microstructures are fabricated and characterized in-depth with scanning electron microscopy as well as infrared spectroscopy and nanoindentation to enable the determination of structure–processability–property relationships. Overall, it is clearly demonstrated that the macromolecular design plays a role in the printability and mechanical properties of the obtained materials.

Machine learning models have been progressively used for predicting materials' properties. These models can be built using pre-existing data and are useful for rapidly screening the physicochemical space of a material, which is astronomically large. However, ML models are inherently interpolative, and their efficacy for searching candidates outside a material's known range of properties is unresolved. Moreover, the performance of an ML model is intricately connected to its learning strategy and the volume of training data. Here, we determine the relationship between the extrapolation ability of an ML model, the size and range of its training dataset, and its learning approach. We focus on a canonical problem of predicting the properties of a copolymer as a function of the sequence of its monomers. Tree search algorithms, which learn the similarities between polymer structures, are found to be inefficient for extrapolation. Conversely, the extrapolation capability of neural networks and XGBoost models, which attempt to learn the underlying functional correlation between the structure and properties of polymers, shows strong correlations with the volume and range of training data. These findings have important implications on ML-based new material development.

Seven isostructural supramolecular adducts, [Ln₂(O₂CC₆H₃(NO₂)₂)₆(DMSO)₄]·4(1,4-(H₂N)₂C₆Me₄) (Ln = Sm (1), Gd (2), Tb (3), Dy (4), Ho (5), Er (6), Y (7)), were synthesized by reacting LnCl₃·6H₂O with potassium 3,5-dinitrobenzoate in acetonitrile in the presence of 2,3,5,6-tetramethyl-1,4-phenylenediamine (DAD) and DMSO, and characterized by X-ray diffraction analysis. The charge transfer (CT) between DAD molecules and binuclear 3,5-dinitrobenzoate fragments gives rise to stacking interactions, which determine the supramolecular structures of complexes 1–7. Optical spectroscopy of complexes 1–7 corroborates the occurrence of significant CT, whereas magnetic studies substantiate the presence of a paramagnetic ion-radical structure which contributes to the magnetic moment of all the complexes and determines the paramagnetism of the yttrium compound 7. In the case of the latter complex, the value of the paramagnetic contribution resulting from CT was determined directly by magnetic measurement. It was demonstrated that this contribution decreases with the lowering of temperature, reflecting the depopulation of the triplet state of the CT complex, the ion-radical pair. A comprehensive EPR study of complex 7 was carried out by means of both continuous-wave (CW) and pulsed EPR spectroscopy in X- and Q-bands. The magnetic properties of complexes 2–6 indicate the prevalence of weak antiferromagnetic interactions within the binuclear fragments. The Dy complex exhibits field-induced single-molecule magnet (SMM) behaviour. The CT in the complex structures was modelled using DFT calculations.

Visualizing singlet oxygen (¹O₂) in biological systems could greatly enhance our understanding of its biological roles and offer new diagnostics and therapeutics. However, ¹O₂ is unstable and highly reactive, making its detection in living systems a significant challenge. To address this, we have developed dually-labelled polymeric micelles designed to trace both the location and levels of ¹O₂.

The endeavor of utilizing non-radiative triplet excitons in RTP and TADF molecules has garnered significant interest in recent studies, presenting a highly desirable yet challenging pursuit. In this investigation, we utilized DFT and TD-DFT computational approaches to anticipate the photophysical characteristics of multifunctional materials, uncovering their significant reliance on the oxidation state and heavy atom influences of the chalcogen group on boron centered D(X)BNA cores, along with substitutions of weak phenylcarbazole (P-CBZ) and strong phenyldimethylacridine (P-DMAC) donors. The calculations demonstrated that both heavy atom (X = O, S, Se, Te) and oxidation (S, SO, SO₂, and Se, SeO) effects caused a decrease in singlet (S₁) and triplet (T₁) energies. Unexpectedly, the first singlet-triplet energy difference (ΔE_ST) values exhibit a systematic decrease with weak donor-based molecules, while they increase with strong donor unit-based molecules with the heavy atom effects. Moreover, the ΔE_ST values decrease systematically with the oxidation effect in both types of donor unit-based molecules. Conversely, the magnitudes of spin–orbit coupling (SOC) increase with heavy atom effects due to the orbital mixing and screening effects of lone pair electrons and decrease with oxidation effects because of their decreased lone pair electrons in both the S₁–T₁ and T₁–S₀ pathways. The elevated SOC and intersystem crossing (ISC) rates in heavy atom-based molecules, and low ΔE_ST and high reverse intersystem crossing (RISC) in oxidation-based molecules, meet the criteria for multifunctional RTP and TADF molecules, respectively.

Retrobiosynthesis tools harness the inherent promiscuities of enzymes for the de novo design of novel biosynthetic pathways to key small molecules. Many existing pathway search algorithms rely on exhaustively enumerating the space of all possible enzymatic reactions using generalized rules, followed by an extensive analysis of the ensuing reaction network to extract candidate pathways for experimental validation. While this approach is comprehensive, many false positive reactions are often generated given the permissiveness of such reaction rules. Here, we have developed DORA-XGB, a enzymatic reaction feasibility classifier. DORA-XGB can be used within our DORAnet framework to assess whether newly enumerated enzymatic reactions and pathways would be feasible. To curate a training dataset for our model, we extracted enzymatic reactions from public databases and screened them for their general thermodynamic feasibility. We then considered alternate reaction centers on known substrates to strategically generate infeasible reactions with high confidence, thereby circumventing the lack of negative data in the literature. In training our model, we also experimented with various molecular fingerprinting techniques and configurations for assembling reaction fingerprints, taking into account not just primary substrate and primary product structures, but cofactor structures as well. Our model's utility is demonstrated through favorable benchmarking against a previously published classifier, the successful recovery of newly published reactions, and the ranking of previously predicted pathways for the biosynthesis of propionic acid from pyruvate.

This research paper reports the synthesis of a super-porous hydrogel of xanthan gum with acrylamide (i.e., XG-SPH) with highly dense interconnected capillary channels and its application as a desiccant material to capture water vapors from humid air. For the generation of the porous structure with interconnected capillary channels, the polymer desiccant was synthesized via gas blowing, foaming and polymerization. The presence of interconnected capillary channels was observed in the scanning electron microscopy (SEM) images. The synthesized desiccant exhibited 0.27 g g⁻¹ adsorption capacity at 50% relative humidity and 25 °C which drastically increased to 1.38 g g⁻¹ at 25 °C and 90% relative humidity which suggested that the hydrophilic nature or the desiccant performance of the synthesized polymer desiccant increased with increasing relative humidity. The main driving force behind this high-water vapor adsorption capacity was the capillary condensation process which facilitated the adsorption or accommodation of more incoming water molecules at higher pressures. The adsorption of water molecules by the capillary condensation mechanism was further supported by the applicability of the type-III adsorption isotherm and the experimental data fitted well with the GAB adsorption isotherm. Moreover, the experimental kinetics data correlated well with the driving force model and indicated that water diffusion within the polymer structure followed a type II diffusion mechanism. The desorption kinetics indicated that the desorption occurred rapidly in the initial desorption stages, and most of the captured water was released within the first hour. Moreover, regenerating XG-SPH was energy efficient as it could be successfully regenerated at 50 °C and used for twenty adsorption–desorption cycles. The desiccant was able to retain almost 70% of its original adsorption capacity in the twentieth adsorption cycle. This suggests that gum polysaccharide-based super-porous hydrogels can extract or capture a considerable amount of water from the atmosphere without using any hygroscopic salt.