Molecular Systems Design & Engineering最新文献

{"title":"Construction of an organic cage-based porous ionic liquid using an aminal tying strategy.","authors":"Aiting Kai, Austin Mroz, Kim E Jelfs, Andrew I Cooper, Marc A Little, Rebecca L Greenaway","doi":"10.1039/d5me00004a","DOIUrl":"https://doi.org/10.1039/d5me00004a","url":null,"abstract":"<p><p>An aminal tying method was applied to post-synthetically modify a flexible organic cage, <b>RCC1</b>, to construct a porous ionic liquid (PIL). The resulting PIL, [<b>RCC1</b>-IM][NTf₂]₆, displayed melting behaviour below 100 °C, a transition to a glass phase on melt-quenching, CO₂ uptake, and its permanent porosity was confirmed using molecular dynamic simulations.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning accelerated discovery of conjugated oligomers for advanced organic photovoltaics†","authors":"Siyan Deng, Jing Xiang Ng and Shuzhou Li","doi":"10.1039/D4ME00188E","DOIUrl":"https://doi.org/10.1039/D4ME00188E","url":null,"abstract":"<p >Machine learning accelerates material discovery which includes selection of candidate small molecules and polymers for high-efficiency organic photovoltaic (OPV) materials. However, conventional machine learning models suffer from data scarcity for conjugated oligomers, crucial for OPV material production. To address this challenge, transfer learning within a graph neural network was introduced to reduce the data requirement while accurately predicting the electronic properties of the conjugated oligomers. By leveraging on transfer learning using original conjugated oligomer data and pre-trained models from the renowned PubChemQC dataset, the limitations posed by insufficient data were mitigated. The models in this study achieved a low mean absolute error, ranging from 0.46 to 0.74 eV, for the HOMO, LUMO, and HOMO–LUMO gap. An original candidate dataset of 3710 conjugated oligomers was constructed for materials discovery, and a high-throughput screening pipeline was developed by integrating the models with density functional theory. This pipeline effectively identified 46 promising conjugated oligomer candidates, showcasing its effectiveness in accelerating the discovery of advanced materials for organic photovoltaics. These results demonstrated the potential of the approach used in this study to overcome data scarcity while accelerating the discovery of new innovative materials in organic electronics.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 413-423"},"PeriodicalIF":3.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00188e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stable n-type organic small-molecule conductor enabled by chemically doped ternary components†","authors":"Bohan Zhou, Ziting Zhong, Runshi Wu, Wenzhao Xiong, Huawei Hu, Anlian Pan, Dafei Yuan and Xiaozhang Zhu","doi":"10.1039/D4ME00192C","DOIUrl":"https://doi.org/10.1039/D4ME00192C","url":null,"abstract":"<p >Chemical doping is a versatile method for tuning the optoelectronic properties of organic semiconductors (OSCs). Compared to p-type doping, achieving stable and efficient n-type doping in OSCs, especially in small molecules, remains a significant challenge. The lack of a universal doping strategy, along with OSCs having deep lowest unoccupied molecular orbital (LUMO) energy levels and high electron mobility, limits the development of n-type doped OSCs. In this work, a ternary system containing the small-molecule OSC 2DQTT-o, with a deep LUMO level and high electron mobility, the n-type dopant N-DMBI, and the polar insulating polymer PEO was developed. With the introduction of PEO, the miscibility, doping level and doping stability were significantly improved. Notably, the ternary doped components showed excellent air stability, retaining 82% of the initial electrical conductivity after exposure to air for 240 h, representing a 32% improvement compared to the system without PEO. Furthermore, the ternary doped films exhibited good thermal stability, retaining 55% of the initial electrical conductivity after heating at 200 °C. In contrast, the two-component doped films decomposed and became insulating.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 357-363"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mesoscale modelling of polymer-mediated adhesion: application to tack tests†","authors":"Aristotelis P. Sgouros, Stefan Knippenberg, Anthony Bocahut, Phillip M. Rauscher, Ben Sikora, Stefano Caputo, Hee-Sung Choi, Vincent Finsy, Maxime Guillaume and Doros N. Theodorou","doi":"10.1039/D4ME00199K","DOIUrl":"https://doi.org/10.1039/D4ME00199K","url":null,"abstract":"<p >We develop a generic computational methodology to understand and predict adhesion between polymers and solid substrates. The motion of coarse-grained polymer segments is tracked <em>via</em> a hybrid particle-field mesoscopic simulation method (BD/kMC) combining Brownian dynamics (BD) and kinetic Monte Carlo (kMC) for the entanglement dynamics as described by the slip-spring model. The method addresses entangled polymer films capped between solid surfaces under both quiescent and nonequilibrium conditions. The latter entail imposing constant rate extension along the aperiodic (normal) direction, while keeping the lateral dimensions constant. Experimentally relevant length scales and elongation rates can be addressed thanks to the coarse-graining inherent in the approach. These simulations are representative of “tack” tests, employed routinely for assessing the performance of soft adhesive materials. The performance of each interface is characterized by the stress–strain curves, yield stress, and toughness. The failure mechanism is determined upon analyzing the evolution of the stress–strain curve and the morphology of the fractured interfaces. The simulations are conducted over a broad parameter space by varying the rate of elongation, the rate constants for attachment/detachment of polymer segments to/from the surface, and the activation length. The latter describes the coupling with the pulling forces exerted on the particles at the interface by the rest of the polymer. Setting the activation length to zero is suitable for describing strong adhesives or highly compressible materials (foams). Under these conditions, toughness is maximized and increases significantly with elongation rate, sometimes leading to chain fracture. With increasing activation length the toughness of the interface decreases and detachment becomes more efficient at higher elongation rates since the increased stress accelerates the detachment process. In all cases considered here, toughness increases monotonically with adhesion. Furthermore, the yield stress increases consistently with increasing elongation rate due to the inability of the polymer to relax the imposed stress.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 394-412"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00199k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks†","authors":"Rodrigo P. Ferreira, Rui Ding, Fengxue Zhang, Haihui Pu, Claire Donnat, Yuxin Chen and Junhong Chen","doi":"10.1039/D4ME00203B","DOIUrl":"https://doi.org/10.1039/D4ME00203B","url":null,"abstract":"<p >Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 345-356"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00203b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The preparation of flame-retardant materials with complex shapes based on a dual-modulus network strategy†","authors":"Xiaoyu Dong, Lingyu Xu, Jiawei Li, Qiangkun Zhang, Zhongjun Cheng, Zhimin Xie, Hanyu Ma, Dongjie Zhang and Yuyan Liu","doi":"10.1039/D4ME00140K","DOIUrl":"https://doi.org/10.1039/D4ME00140K","url":null,"abstract":"<p >Flame-retardant thermosetting polymers are extensively used in construction materials and aerospace applications due to their inherent stability and performance characteristics. Traditional processing methods, however, are limited to producing simple geometries such as strips, blocks, and plates. Additionally, small molecule flame retardants exist in the resin matrix in a free form, and as the resin is used over time, these small molecule flame retardants tend to migrate, which deteriorates the flame-retardant performance of the material. Herein, we synthesized a flame retardant containing P and N elements with a double bond, which also serves as a curing agent, through molecular design and applied it in an acrylate–epoxy resin dual-modulus network system. Initial photopolymerization facilitated the creation of a low-modulus acrylate network, endowing the material with significant flexibility and allowing for arbitrary shaping. The double bonds present in the designed flame retardant ensure its integration into the acrylate network during photopolymerization, thereby mitigating migration issues. Subsequently, this flexible material undergoes thermal curing to form a high-modulus epoxy resin network, increasing the material's tensile modulus by up to 2500 times, tensile strength by up to 300 times, and glass transition temperature by up to 180 °C, resulting in a rigid material. Therefore, this work introduces an innovative approach to fabricating flame-retardant thermosetting materials with complex shapes while effectively reducing the migration of flame retardant molecules within the resin matrix.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 364-376"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating structural biophysical features for antigen-binding fragment crystallization via machine learning†","authors":"Krishna Gopal Chattaraj, Joana Ferreira, Allan S. Myerson and Bernhardt L. Trout","doi":"10.1039/D4ME00187G","DOIUrl":"https://doi.org/10.1039/D4ME00187G","url":null,"abstract":"<p >Antibody-based therapeutics continue to be an important pharmaceutical development modality. Crystallization of antibodies is important for structural characterization, but in addition has the potential for use as a separation method and for use as a dosage form. Nevertheless, bringing about controlled crystallization of an antibody remains a challenging task due to its large size, high degree of segmental flexibility, and the intricacy of all the occurring interactions (<em>e.g.</em>, protein–protein interactions, protein–solvent interactions, <em>etc.</em>). Methods to predict important contact sites could help to develop such crystallization methods. However, limited data and understanding have hitherto not allowed the development of such robust methods. This study employs machine learning combined with <em>in silico</em> modelling of crystal structures using available experimental structures to identify the crucial physicochemical features necessary for successful antibody crystallization in an attempt to remedy that gap. The developed method can with good accuracy distinguish crystal-site residues from non-crystal-site residues. A set of 510 descriptors is utilized to characterize each residue, which is treated as a distinct data point. Moreover, new algorithms have been developed to design novel descriptors that improve the model's predictive capabilities. Fragment antigen-binding (Fab) regions are investigated due to the scarcity of full-length monoclonal antibodies (mAbs) crystal structures. The current findings show that the extreme gradient boosting (XGBoost) algorithm effectively identifies crystal site residues, as evidenced by an AUPRC value that is more than 3-fold higher than that of the baseline model. The top-ranked descriptors indicate that crystal-site residues are primarily characterized by solvent-exposed residues with high spatial aggregation propensity (SAP), signifying hydrophobic patches, and their immediate surface-exposed neighbors. Moreover, these high SAP residues are often surrounded by other solvent-exposed residues that are either polar, charged, or both. In contrast, residues not involved in crystal interfaces generally lack these essential features, though some might be excluded due to specific crystal lattice arrangements. Additionally, reducing the feature set from 510 to the top 15% in the XGBoost model yields similar performance while significantly simplifying the model.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 377-393"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00187g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bio-inspired approach to engineering water-responsive, mechanically-adaptive materials†","authors":"Daseul Jang, Yu-Tai Wong and LaShanda T. J. Korley","doi":"10.1039/D4ME00177J","DOIUrl":"https://doi.org/10.1039/D4ME00177J","url":null,"abstract":"<p >Inspired by a diverse array of hierarchical structures and mechanical function in spider silk, we leverage building blocks that can form non-covalent interactions to develop mechanically-tunable and water-responsive composite materials <em>via</em> hydrogen bonding modulation. Specifically, self-assembling peptide blocks consisting of poly(β-benzyl-<small>L</small>-aspartate) (PBLA) are introduced into a hydrophilic polyurea system. Using these peptide–polyurea hybrids (PPUs) as a hierarchical matrix, cellulose nanocrystals (CNCs) are incorporated to diversify the self-assembled nanostructures of PPUs through matrix–filler interactions. Our findings reveal that higher PBLA content in the PPUs reduces the magnitude of the stiffness differential due to the physical crosslinking induced by the peptide blocks. Additionally, the inclusion of CNCs in the PPU matrix increases the storage modulus in the dry state <img> but also diminishes the wet-state modulus <img> due to the shift of physical associations from peptidic arrangements to PBLA–CNC interactions, resulting in variations in the morphology of the PPU/CNC nanocomposites. This molecular design strategy allows for the development of adaptable materials with a broad range of water-responsive storage modulus switching <img>, spanning from ∼70 MPa to ∼400 MPa. This investigation highlights the potential of harnessing peptide assembly and peptide–cellulose interactions to achieve mechanical enhancement and water-responsiveness, providing insights for engineering next-generation responsive materials.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 264-278"},"PeriodicalIF":3.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00177j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reweighting configurations generated by transferable, machine learned models for protein sidechain backmapping†","authors":"Jacob I. Monroe","doi":"10.1039/D4ME00198B","DOIUrl":"https://doi.org/10.1039/D4ME00198B","url":null,"abstract":"<p >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.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 298-313"},"PeriodicalIF":3.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00198b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular analysis and design using generative artificial intelligence via multi-agent modeling","authors":"Isabella Stewart and Markus J. Buehler","doi":"10.1039/D4ME00174E","DOIUrl":"10.1039/D4ME00174E","url":null,"abstract":"<p >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 <em>via</em> 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.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 314-337"},"PeriodicalIF":3.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}