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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The research of this project integrates and advances state-of-the-art theoretical methods for geometrically complex BCP assembly (Grason), synthesis of precisely-defined BCP architectures (Coughlin), and quantitative and multi-scale approaches to morphological characterization of BCP assembly (Thomas).
The research performed integrated and advanced state-of-the-art theoretical methods for geometrically-complex BCP assembly (Grason), synthesis of precisely-defined BCP architectures (Coughlin), and quantitative and multi-scale approaches to morphological characterization of BCP assembly (Thomas).
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The ongoing novel coronavirus pandemic (COVID-19) has highlighted the need for new therapeutics to counter the threat of emerging viral pathogens. The main proteases are a promising target for developing antiviral inhibitors. In this work, we utilized a novel combination of artificial intelligence-driven iterative design of covalent inhibitor candidates, physics-based computational modeling of protein-inhibitor interactions, and “All in One” Native MS biophysical assay screening and characterization of therapeutic candidates. With our existing expertise in hit generation using a particular scaffold as a starting point, we first generated tens of thousands of compounds that preserve the key scaffold. In order to optimize the candidates, we calculated about 136 descriptors consisting of 2D and 3D features for molecules targeting the SARS-CoV-2 Main protease (Mpro). These compounds were initially filtered according to properties and further sorted by predicted binding affinity using our automated docking modeling and machine learning methods. We tested a handful of candidates and identified two as inhibitors of Mpro with micromolar affinities.
Final technical report on DOE project conducted at Texas A & M University Dept. of Materials Science and Engineering concerning finding and understanding the design rules for triply periodic, bicontinuous morphological microdomain structures of block copolymers.
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Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.
Advances in the evolving field of atomistic simulations promise important insights for the design and fundamental understanding of novel molecular photoswitches. Here, in this study, we use state-of-the-art enhanced simulation techniques to unravel the complex, multistep chemistry of donor–acceptor Stenhouse adducts (DASAs). Our reaction discovery workflow consists of enhanced sampling for efficient chemical space exploration, refinement of newly observed pathways with more accurate ab initio electronic structure calculations, and structural modifications to introduce design principles within future generations of DASAs. We showcase our discovery workflow by not only recovering the full photoswitching mechanism of DASA but also predicting a plethora of new plausible thermal pathways and suggesting a way for their experimental validation. Furthermore, we illustrate the tunability of these newly discovered reactions, leading to a potential avenue for controlling DASA dynamics through multiple external stimuli. Overall, these insights could offer alternative routes to increase the efficiency and control of DASA’s photoswitching mechanism, providing new elements to design more complex light-responsive materials.
The proposed research aims at developing a computational framework that allows the de novo design of sequence defined polymers for molecular recognition. This framework will include the prediction of the 1) accessible backbone conformational space in solution environments for small to medium polymer length and 2) how side chains of different size and functionality can be used to design convergent or divergent receptor configurations. A structural database inspired by the protein database will be build that can be used to construct larger scaffolds based on smaller sequences
The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.
Abstract Through advances in molecular design, understanding of processing parameters, and development of non‐traditional device fabrication techniques, the field of wearable and implantable skin‐inspired devices is rapidly growing interest in the consumer market. Like previous technological advances, economic growth and efficiency is anticipated, as these devices will enable an augmented level of interaction between humans and the environment. However, the parallel growing electronic waste that is yet to be addressed has already left an adverse impact on the environment and human health. Looking forward, it is imperative to develop both human‐ and environmentally‐friendly electronics, which are contingent on emerging recyclable, biodegradable, and biocompatible polymer technologies. This review provides definitions for recyclable, biodegradable, and biocompatible polymers based on reported literature, an overview of the analytical techniques used to characterize mechanical and chemical property changes, and standard policies for real‐life applications. Then, various strategies in designing the next‐generation of polymers to be recyclable, biodegradable, or biocompatible with enhanced functionalities relative to traditional or commercial polymers are discussed. Finally, electronics that exhibit an element of recyclability, biodegradability, or biocompatibility with new molecular design are highlighted with the anticipation of integrating emerging polymer chemistries into future electronic devices.
Inspired by natural photosynthesis, the mission of the AMPED EFRC was to develop the fundamental molecular basis for solar-driven water oxidation and carbon dioxide reduction catalysis with dye-sensitized photoelectrodes. From 2014-2022, the Center advanced the state-of-the-art in molecule-decorated photoanodes for water oxidation and broke new ground in the development of photocathodes for CO 2 reduction. A collaborative approach brought together experts in inorganic synthesis, organometallic catalysis, interfacial materials synthesis, photoelectrochemistry, and time-resolved dynamics to address by Grand Challenges in the 2007 DOE BESAC report. Since 2009, the UNC EFRC has published 314 peer-reviewed journal articles, and 2 book chapters. More specifically there were 146 peer-reviewed publications,1-146 during the 2014-20 funding cycle, with 80% supported solely by the EFRC, and about half resulting from collaborations among multiple PIs. Three major scientific research areas emerged from AMPED that are described below with details of the most impactful advances for solar energy research.
We present a thorough analysis of active learning strategies for polymer sequence engineering and design.
One of the greatest obstacles to achieving implantable electronics with long-term functionality and minimized inflammatory reactions is the immune-mediated foreign-body response (FBR). Recently, semiconducting polymers with mixed electron–ion conductivity have been demonstrated as promising candidates to achieve direct electrical interfacing on bio-tissues. However, there is limited understanding of their immune compatibility in vivo, and strategies for minimizing the FBR through molecular design remain underexplored. Here we introduce a set of molecular design strategies for enhancing the immune compatibility of semiconducting polymers. Specifically, we show that selenophene, when incorporated in the backbone, can mitigate the FBR by suppressing macrophage activation. In addition, side-chain functionalization with immunomodulatory groups decreases the FBR further by downregulating the expression of inflammatory biomarkers. Together, our synthesized polymers achieve suppression of the FBR by as much as 68% (as indicated by the collagen density). In the meantime, these immune-compatible designs still provide a high charge-carrier mobility of around 1 cm 2 V −1 s −1 . In conclusion, we anticipate that such immune-compatible design principles can be translated to a variety of conjugated polymers to suppress the FBR for implantable applications.
As one of the simple but most effective molecular design strategies, side-chain engineering has been widely employed to modify the photoelectric properties of active layer materials for boosting the photovoltaic performance of organic solar cells (OSCs). In this work, a functionalized small molecule acceptor (SMA) named BTSi-4F with a bulky siloxane-terminated solubilizing group as side-chains, derived from a classical SMA of Y6, was designed and synthesized. The results demonstrate that the introduction of siloxane-functional terminated groups into SMA not only affects the optical absorption and molecular energy levels, but also regulates the miscibility between the polymer donor and SMA. Compared to the original Y6, BTSi-4F exhibits a better solubility, upshifted lowest unoccupied molecular orbital (LUMO) energy level, more ordered molecular packing, and higher electron-mobility. Matched with a wide bandgap polymer donor PM6, the chlorobenzene-processed OSCs based on PM6:BTSi-4F achieved a superior power conversion efficiency (PCE) of 16.6% with both high open-circuit voltage (V oc ) of 0.90 V and high fill factor (FF) of 0.77, while the devices based on PM6:Y6 obtained a much lower PCE of 13.0% with a V oc of 0.81 V and FF of 0.69 under the same conditions. This work offers a promising molecular design strategy of siloxane-terminated side chain engineering to develop high-performance SMAs for efficient OSCs.
Nonfused-ring electron acceptors (NFREA)s have been proposed as alternatives to fused-ring acceptors in organic photovoltaics (OPV)s. The simpler molecular design of NFREAs results in a reduced synthetic complexity, potentially lowering manufacturing costs, and easier access to desired optoelectronic properties by structural modifications. However, they have not seen the same rise in popularity as fused nonfullerene acceptors (NFA)s due to their lower performances in devices. In this work, we explore structure–property relationships of three NFREA molecules based on thieno[3,2-b]thiophene (TT) and benzo[1,2-b:4,5-b′]dithiophene (BDT) cores and malononitrile functionalized-isatin end group acceptors. We describe their synthetic routes, computational analysis, and photophysical and electronic properties. Here, we investigated the effect of changing the core from TT to BDT on electronic properties of the molecules─a result of raising/lowering of the frontier molecular orbital (FMO) levels. We also explored the effect of branched vs nonbranched alkyl groups on the morphological and blend-capabilities in bulk heterojunction (BHJ) OPV devices. These effects were then reflected in the overall device performances, primarily due to favorable FMO energy alignment between the NFREA and polymer. To investigate the behavior of the excited states of the NFREAs within the active layer of the devices, we performed time-resolved microwave conductivity and transient absorption experiments, which confirmed that the TT molecules exhibited very low conductance while the BDT molecule showed moderate photoconductance. Our combined theoretical, synthetic, device, and charge carrier dynamics studies of these materials reveal the central role that structure–property relationships play in future molecular designs of NFREAs that can perform as well as their fused counterparts.