Molecular Systems Design & Engineering最新文献

Multiscale modeling requires the linking of models at different levels of detail, with the goal of gaining accelerations from lower fidelity models while recovering fine details from higher resolution models. Communication across resolutions is particularly important in modeling soft matter, where tight couplings exist between molecular-level details and mesoscale structures. While multiscale modeling of biomolecules has become a critical component in exploring their structure and self-assembly, backmapping from coarse-grained to fine-grained, or atomistic, representations presents a challenge, despite recent advances through machine learning. A major hurdle, especially for strategies utilizing machine learning, is that backmappings can only approximately recover the atomistic ensemble of interest. We demonstrate conditions for which backmapped configurations may be reweighted to exactly recover the desired atomistic ensemble. By training separate decoding models for each sidechain type, we develop an algorithm based on normalizing flows and geometric algebra attention to autoregressively propose backmapped configurations for any protein sequence. Critical for reweighting with modern protein force fields, our trained models include all hydrogen atoms in the backmapping and make probabilities associated with atomistic configurations directly accessible. We also demonstrate, however, that reweighting is extremely challenging despite state-of-the-art performance on recently developed metrics and generation of configurations with low energies in atomistic protein force fields. Through detailed analysis of configurational weights, we show that machine-learned backmappings must not only generate configurations with reasonable energies, but also correctly assign relative probabilities under the generative model. These are broadly important considerations in generative modeling of atomistic molecular configurations.

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human–AI and AI–AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

The present study explores the comprehensive investigations of two methoxy-oriented chalcone structures (HK1 and HK2), each featuring distinct halogen substituents (chlorine and bromine). The crystals of the derivatives were grown and confirmed via single-crystal X-ray diffraction (XRD), revealing that HK1 crystallizes in the orthorhombic system with the space group Pbca, while HK2 crystallizes in the monoclinic system with the space group P2₁/c. Intermolecular interactions, such as hydrogen bonding, π–π stacking, and van der Waals forces, were examined for their role in molecular assembly. Hirshfeld surface analysis and enrichment ratio provided further insights into these intermolecular interactions within the lattice. Density functional theory (DFT) calculations using the B3LYP functional and 6-311++G (d,p) basis set was employed to explore the electronic structure and physicochemical properties. Quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses elucidated the topology of these compounds. In silico biological studies of the derivatives were also carried out, focusing on their inhibitory potential targeting monoamine oxidase (MAO-A and MAO-B) enzymes. Drug-likeness was evaluated through ADME-T profiling predictions, followed by molecular docking and dynamics simulations to determine the favorable binding configurations within the MAOs. Dynamics simulations over a 100 ns period confirmed the stability of the ligand–protein complexes. Overall, the present study offers a deeper understanding of the structural intricacies of the reported molecules by providing valuable insights into their chemical and biological properties through molecular interactions.

The micro-channel structure in polymers has excellent properties and is widely used in biochemistry instruments, optical sensor devices and so on. At present, numerous challenges such as low surface quality and unstable formation are faced during the fabrication of internal polymer micro-channel structures, leading to functions that do not meet expectations. In this paper, a mathematical model for channel formation in polymers is established using phase field theory, and the deformation mechanism of the microstructure driven by surface energy was studied. Next, the micro-nano-structure evolution of the polymer was simulated, and the morphology of single-channel, double-channel and Z-shaped-channel structures was studied. Finally, a comparison test of the formed structure under the action of a single temperature field and thermal-electric coupling field was carried out, and experimental results were found to be consistent with simulation results.

Size-dependent applications of biomaterials are increasing day by day, and rod-shaped biomaterials are drawing researchers attention for their different enhanced properties. Different types of chemicals are used to modify the crystal structure of hydroxyapatites (HAps); however, in this research, plant extract (Aloe vera) was chosen to control the shape of nano-crystalline HAps. This research focused on synthesizing rod-shaped hydroxyapatite using a non-toxic, environmentally friendly, low-cost, and widely available natural source. Hydrothermal technique was used to synthesize nano-hydroxyapatite (nHAp), where different volumes (0, 2.5, 5.0, and 10 mL) of plant extract were added to a water medium with raw materials [Ca(OH)₂ and H₃PO₄]. XRD, FESEM, XPS, FTIR, and optical bandgap energy calculations confirmed the formation of nHAp. Its texture coefficient and preference growth values showed that the (0 0 2) and (0 0 4) planes were the preferred growth direction when Aloe vera extract was used. Crystallite sizes were in the range of 30–72 nm, as per XRD data, and the 88–107 nm length and 31–38 nm width of rod-shaped particles was confirmed by FESEM data. Very low bandgap energies in the range of 3.56–3.81 eV were found for the synthesized nHAp. There were no significant differences in the binding energy according to XPS data, and the calculated as well as direct ratio of Ca/P and O/Ca confirmed the formation of similar nHAps.

Retraction of ‘Heteroatoms chemical tailoring of aluminum nitrite nanotubes as biosensors for 5-hydroxyindole acetic acid (a biomarker for carcinoid tumors): insights from a computational study’ by Chioma B. Ubah et al., Mol. Syst. Des. Eng., 2024, 9, 832–846, https://doi.org/10.1039/D4ME00019F.

We here report rodlike liquid-crystalline (LC) molecules consisting of bicyclohexyl and linear carbonate moieties connected through flexible spacers for the development of nanostructured ion-conductive materials. The molecular assemblies of the linear carbonate-based rodlike compounds mixed with a lithium salt provide 2D ion-conductive pathways in the smectic LC phases. The LC materials containing polar oligooxyethylene spacers coupled with linear carbonate moieties have been shown to function as efficient ion conductors, while those containing nonpolar alkylene spacers form thermally stable and ordered smectic LC structures. Molecular dynamics simulations provide insights into the conformation and packing of the molecules containing oligooxyethylene spacers in the LC phases. The combination of flexible oligooxyethylene chains and linear carbonates may lead to design of new LC electrolytes with highly mobile 2D nanochannels for applications in energy devices.

Adsorption-based processes are showing substantial potential for carbon capture. Due to the vast space of potential solid adsorbents and their influence on the process performance, the choice of the material is not trivial but requires systematic approaches. In particular, the material choice should be based on the performance of the resulting process. In this work, we present a method for the process-based screening of porous materials for pressure and vacuum swing adsorption. The method is based on an equilibrium process model that incorporates one-dimensional classical density functional theory (1D-DFT) and the PC-SAFT equation of state. Thereby, the presented method can efficiently screen databases of potential adsorbents and identify the best-performing materials as well as the corresponding optimized process conditions for a specific carbon capture application. We apply our method to a point-source carbon capture application at a cement plant. The results show that the process model is crucial to evaluating the performance of adsorbents instead of relying solely on material heuristics. Furthermore, we enhance our approach through multi-objective optimization and demonstrate for materials with high performance that our method is able to capture the trade-offs between two process objectives, such as specific work and purity. The presented method thus provides an efficient screening tool for adsorbents to maximize process performance.

Phase separation in multicomponent mixtures is of significant interest in both fundamental research and technology. Although the thermodynamic principles governing phase equilibria are straightforward, practical determination of equilibrium phases and constituent compositions for multicomponent systems is often laborious and computationally intensive. Here, we present a machine-learning workflow that simplifies and accelerates phase-coexistence calculations. We specifically analyze capabilities of neural networks to predict the number, composition, and relative abundance of equilibrium phases of systems described by Flory–Huggins theory. We find that incorporating physics-informed material constraints into the neural network architecture enhances the prediction of equilibrium compositions compared to standard neural networks with minor errors along the boundaries of the stable region. However, introducing additional physics-informed losses does not lead to significant further improvement. These errors can be virtually eliminated by using machine-learning predictions as a warm-start for a subsequent optimization routine. This work provides a promising pathway to efficiently characterize multicomponent phase coexistence.

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.