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Item Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster Jastrow ansatz for electronic structure(Royal Society of Chemistry, 2023) Motta, Mario; Sung, Kevin J.; Whaley, K. Birgitta; Head-Gordon, Martin; Shee, JamesA prominent goal in quantum chemistry is to solve the molecular electronic structure problem for ground state energy with high accuracy. While classical quantum chemistry is a relatively mature field, the accurate and scalable prediction of strongly correlated states found, e.g., in bond breaking and polynuclear transition metal compounds remains an open problem. Within the context of a variational quantum eigensolver, we propose a new family of ansatzes which provides a more physically appropriate description of strongly correlated electrons than a unitary coupled cluster with single and double excitations (qUCCSD), with vastly reduced quantum resource requirements. Specifically, we present a set of local approximations to the unitary cluster Jastrow wavefunction motivated by Hubbard physics. As in the case of qUCCSD, exactly computing the energy scales factorially with system size on classical computers but polynomially on quantum devices. The local unitary cluster Jastrow ansatz removes the need for SWAP gates, can be tailored to arbitrary qubit topologies (e.g., square, hex, and heavy-hex), and is well-suited to take advantage of continuous sets of quantum gates recently realized on superconducting devices with tunable couplers. The proposed family of ansatzes demonstrates that hardware efficiency and physical transparency are not mutually exclusive; indeed, chemical and physical intuition regarding electron correlation can illuminate a useful path towards hardware-friendly quantum circuits.Item Cell behaviors underlying Myxococcus xanthus aggregate dispersal(American Society for Microbiology, 2023) Murphy, Patrick; Comstock, Jessica; Khan, Trosporsha; Zhang, Jiangguo; Welch, Roy; Igoshin, Oleg A.; Center for Theoretical Physical BiologyThe soil bacterium Myxococcus xanthus is a model organism with a set of diverse behaviors. These behaviors include the starvation-induced multicellular development program, in which cells move collectively to assemble multicellular aggregates. After initial aggregates have formed, some will disperse, with smaller aggregates having a higher chance of dispersal. Initial aggregation is driven by two changes in cell behavior: cells slow down inside of aggregates and bias their motion by reversing direction less frequently when moving toward aggregates. However, the cell behaviors that drive dispersal are unknown. Here, we use fluorescent microscopy to quantify changes in cell behavior after initial aggregates have formed. We observe that after initial aggregate formation, cells adjust the bias in reversal timings by initiating reversals more rapidly when approaching unstable aggregates. Using agent-based modeling, we then show dispersal is predominantly generated by this change in bias, which is strong enough to overcome slowdown inside aggregates. Notably, the change in reversal bias is correlated with the nearest aggregate size, connecting cellular activity to previously observed correlations between aggregate size and fate. To determine if this connection is consistent across strains, we analyze a second M. xanthus strain with reduced levels of dispersal. We find that far fewer cells near smaller aggregates modified their bias. This implies that aggregate dispersal is under genetic control, providing a foundation for further investigations into the role it plays in the life cycle of M. xanthus.Item A scientific machine learning framework to understand flash graphene synthesis(Royal Society of Chemistry, 2023) Sattari, Kianoosh; Eddy, Lucas; Beckham, Jacob L.; Wyss, Kevin M.; Byfield, Richard; Qian, Long; Tour, James M.; Lin, Jian; NanoCarbon Center; Welch Institute for Advanced MaterialsFlash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promises in scalability and performance, attempts to explore the reaction mechanism have been limited due to the complexities involved in the FFE process. Data-driven machine learning (ML) models effectively account for the complexities, but the model training requires a considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 ± 0.05 and an average RMSE of 12.1% ± 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 ± 0.05 and an RMSE of 14.3% ± 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes.Item Radical ligand transfer: a general strategy for radical functionalization(Beilstein-Institut, 2023) Jr, David T. Nemoto; Bian, Kang-Jie; Kao, Shih-Chieh; West, Julian G.The place of alkyl radicals in organic chemistry has changed markedly over the last several decades, evolving from challenging-to-generate “uncontrollable” species prone to side reactions to versatile reactive intermediates enabling construction of myriad C–C and C–X bonds. This maturation of free radical chemistry has been enabled by several advances, including the proliferation of efficient radical generation methods, such as hydrogen atom transfer (HAT), alkene addition, and decarboxylation. At least as important has been innovation in radical functionalization methods, including radical–polar crossover (RPC), enabling these intermediates to be engaged in productive and efficient bond-forming steps. However, direct engagement of alkyl radicals remains challenging. Among these functionalization approaches, a bio-inspired mechanistic paradigm known as radical ligand transfer (RLT) has emerged as a particularly promising and versatile means of forming new bonds catalytically to alkyl radicals. This development has been driven by several key features of RLT catalysis, including the ability to form diverse bonds (including C–X, C–N, and C–S), the use of simple earth abundant element catalysts, and the intrinsic compatibility of this approach with varied radical generation methods, including HAT, radical addition, and decarboxylation. Here, we provide an overview of the evolution of RLT catalysis from initial studies to recent advances and provide a conceptual framework we hope will inspire and enable future work using this versatile elementary step.Item Battery metal recycling by flash Joule heating(AAAS, 2023) Chen, Weiyin; Chen, Jinhang; Bets, Ksenia V.; Salvatierra, Rodrigo V.; Wyss, Kevin M.; Gao, Guanhui; Choi, Chi Hun; Deng, Bing; Wang, Xin; Li, John Tianci; Kittrell, Carter; La, Nghi; Eddy, Lucas; Scotland, Phelecia; Cheng, Yi; Xu, Shichen; Li, Bowen; Tomson, Mason B.; Han, Yimo; Yakobson, Boris I.; Tour, James M.; Welch Institute for Advanced Materials; NanoCarbon Center; Applied Physics Program; Smalley-Curl InstituteThe staggering accumulation of end-of-life lithium-ion batteries (LIBs) and the growing scarcity of battery metal sources have triggered an urgent call for an effective recycling strategy. However, it is challenging to reclaim these metals with both high efficiency and low environmental footprint. We use here a pulsed dc flash Joule heating (FJH) strategy that heats the black mass, the combined anode and cathode, to >2100 kelvin within seconds, leading to ~1000-fold increase in subsequent leaching kinetics. There are high recovery yields of all the battery metals, regardless of their chemistries, using even diluted acids like 0.01 M HCl, thereby lessening the secondary waste stream. The ultrafast high temperature achieves thermal decomposition of the passivated solid electrolyte interphase and valence state reduction of the hard-to-dissolve metal compounds while mitigating diffusional loss of volatile metals. Life cycle analysis versus present recycling methods shows that FJH significantly reduces the environmental footprint of spent LIB processing while turning it into an economically attractive process.Item MHC-II dynamics are maintained in HLA-DR allotypes to ensure catalyzed peptide exchange(Springer Nature, 2023) Abualrous, Esam T.; Stolzenberg, Sebastian; Sticht, Jana; Wieczorek, Marek; Roske, Yvette; Günther, Matthias; Dähn, Steffen; Boesen, Benedikt B.; Calvo, Marcos Martínez; Biese, Charlotte; Kuppler, Frank; Medina-García, Álvaro; Álvaro-Benito, Miguel; Höfer, Thomas; Noé, Frank; Freund, ChristianPresentation of antigenic peptides by major histocompatibility complex class II (MHC-II) proteins determines T helper cell reactivity. The MHC-II genetic locus displays a large degree of allelic polymorphism influencing the peptide repertoire presented by the resulting MHC-II protein allotypes. During antigen processing, the human leukocyte antigen (HLA) molecule HLA-DM (DM) encounters these distinct allotypes and catalyzes exchange of the placeholder peptide CLIP by exploiting dynamic features of MHC-II. Here, we investigate 12 highly abundant CLIP-bound HLA-DRB1 allotypes and correlate dynamics to catalysis by DM. Despite large differences in thermodynamic stability, peptide exchange rates fall into a target range that maintains DM responsiveness. A DM-susceptible conformation is conserved in MHC-II molecules, and allosteric coupling between polymorphic sites affects dynamic states that influence DM catalysis. As exemplified for rheumatoid arthritis, we postulate that intrinsic dynamic features of peptide–MHC-II complexes contribute to the association of individual MHC-II allotypes with autoimmune disease.Item Engineering chirality at wafer scale with ordered carbon nanotube architectures(Springer Nature, 2023) Doumani, Jacques; Lou, Minhan; Dewey, Oliver; Hong, Nina; Fan, Jichao; Baydin, Andrey; Zahn, Keshav; Yomogida, Yohei; Yanagi, Kazuhiro; Pasquali, Matteo; Saito, Riichiro; Kono, Junichiro; Gao, Weilu; Carbon Hub; Smalley-Curl InstituteCreating artificial matter with controllable chirality in a simple and scalable manner brings new opportunities to diverse areas. Here we show two such methods based on controlled vacuum filtration - twist stacking and mechanical rotation - for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and large circular dichroism (CD). By controlling the stacking angle and handedness in the twist-stacking approach, we maximize the CD response and achieve a high deep-ultraviolet ellipticity of 40 ± 1 mdeg nm−1. Our theoretical simulations using the transfer matrix method reproduce the experimentally observed CD spectra and further predict that an optimized film of twist-stacked CNTs can exhibit an ellipticity as high as 150 mdeg nm−1, corresponding to a g factor of 0.22. Furthermore, the mechanical rotation method not only accelerates the fabrication of twisted structures but also produces both chiralities simultaneously in a single sample, in a single run, and in a controllable manner. The created wafer-scale objects represent an alternative type of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures and can be used to explore chiral phenomena and develop chiral photonic and optoelectronic devices.Item High-temperature electrothermal remediation of multi-pollutants in soil(Springer Nature, 2023) Deng, Bing; Carter, Robert A.; Cheng, Yi; Liu, Yuan; Eddy, Lucas; Wyss, Kevin M.; Ucak-Astarlioglu, Mine G.; Luong, Duy Xuan; Gao, Xiaodong; JeBailey, Khalil; Kittrell, Carter; Xu, Shichen; Jana, Debadrita; Torres, Mark Albert; Braam, Janet; Tour, James M.; NanoCarbon Center and the Rice Advanced Materials Institute; Smalley-Curl InstituteSoil contamination is an environmental issue due to increasing anthropogenic activities. Existing processes for soil remediation suffer from long treatment time and lack generality because of different sources, occurrences, and properties of pollutants. Here, we report a high-temperature electrothermal process for rapid, water-free remediation of multiple pollutants in soil. The temperature of contaminated soil with carbon additives ramps up to 1000 to 3000 °C as needed within seconds via pulsed direct current input, enabling the vaporization of heavy metals like Cd, Hg, Pb, Co, Ni, and Cu, and graphitization of persistent organic pollutants like polycyclic aromatic hydrocarbons. The rapid treatment retains soil mineral constituents while increases infiltration rate and exchangeable nutrient supply, leading to soil fertilization and improved germination rates. We propose strategies for upscaling and field applications. Techno-economic analysis indicates the process holds the potential for being more energy-efficient and cost-effective compared to soil washing or thermal desorption.Item Heterogeneity in M. tuberculosis β-lactamase inhibition by Sulbactam(Springer Nature, 2023) Malla, Tek Narsingh; Zielinski, Kara; Aldama, Luis; Bajt, Sasa; Feliz, Denisse; Hayes, Brendon; Hunter, Mark; Kupitz, Christopher; Lisova, Stella; Knoska, Juraj; Martin-Garcia, Jose Manuel; Mariani, Valerio; Pandey, Suraj; Poudyal, Ishwor; Sierra, Raymond G.; Tolstikova, Alexandra; Yefanov, Oleksandr; Yoon, Chung Hong; Ourmazd, Abbas; Fromme, Petra; Schwander, Peter; Barty, Anton; Chapman, Henry N.; Stojkovic, Emina A.; Batyuk, Alexander; Boutet, Sébastien; Phillips, George N.; Pollack, Lois; Schmidt, MariusFor decades, researchers have elucidated essential enzymatic functions on the atomic length scale by tracing atomic positions in real-time. Our work builds on possibilities unleashed by mix-and-inject serial crystallography (MISC) at X-ray free electron laser facilities. In this approach, enzymatic reactions are triggered by mixing substrate or ligand solutions with enzyme microcrystals. Here, we report in atomic detail (between 2.2 and 2.7 Å resolution) by room-temperature, time-resolved crystallography with millisecond time-resolution (with timepoints between 3 ms and 700 ms) how the Mycobacterium tuberculosis enzyme BlaC is inhibited by sulbactam (SUB). Our results reveal ligand binding heterogeneity, ligand gating, cooperativity, induced fit, and conformational selection all from the same set of MISC data, detailing how SUB approaches the catalytic clefts and binds to the enzyme noncovalently before reacting to a trans-enamine. This was made possible in part by the application of singular value decomposition to the MISC data using a program that remains functional even if unit cell parameters change up to 3 Å during the reaction.Item Machine learning coarse-grained potentials of protein thermodynamics(Springer Nature, 2023) Majewski, Maciej; Pérez, Adrià; Thölke, Philipp; Doerr, Stefan; Charron, Nicholas E.; Giorgino, Toni; Husic, Brooke E.; Clementi, Cecilia; Noé, Frank; De Fabritiis, Gianni; Center for Theoretical Biological PhysicsA generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.Item Protein target highlights in CASP15: Analysis of models by structure providers(Wiley, 2023) Alexander, Leila T.; Durairaj, Janani; Kryshtafovych, Andriy; Abriata, Luciano A.; Bayo, Yusupha; Bhabha, Gira; Breyton, Cécile; Caulton, Simon G.; Chen, James; Degroux, Séraphine; Ekiert, Damian C.; Erlandsen, Benedikte S.; Freddolino, Peter L.; Gilzer, Dominic; Greening, Chris; Grimes, Jonathan M.; Grinter, Rhys; Gurusaran, Manickam; Hartmann, Marcus D.; Hitchman, Charlie J.; Keown, Jeremy R.; Kropp, Ashleigh; Kursula, Petri; Lovering, Andrew L.; Lemaitre, Bruno; Lia, Andrea; Liu, Shiheng; Logotheti, Maria; Lu, Shuze; Markússon, Sigurbjörn; Miller, Mitchell D.; Minasov, George; Niemann, Hartmut H.; Opazo, Felipe; Phillips Jr, George N.; Davies, Owen R.; Rommelaere, Samuel; Rosas-Lemus, Monica; Roversi, Pietro; Satchell, Karla; Smith, Nathan; Wilson, Mark A.; Wu, Kuan-Lin; Xia, Xian; Xiao, Han; Zhang, Wenhua; Zhou, Z. Hong; Fidelis, Krzysztof; Topf, Maya; Moult, John; Schwede, TorstenWe present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.Item Niobium Oxide Photocatalytically Oxidizes Ammonia in Water at Ambient Conditions(SciELO, 2024) Elias, Welman; Clark, Chelsea; Heck, Kimberly; Arredondo, Jacob; Wang, Bo; Toro, Andras; Kürtib, László; Wong, Michael; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water TreatmentAmmonia contamination in water is a significant environmental issue since it is toxic and leads to eutrophication. Photocatalysis has been investigated as a strategy for ammonia degradation but can potentially form toxic nitrite (NO2–) and nitrate (NO3–) byproducts. This work reports on the ability of niobium oxide (Nb2O5) to photocatalytically oxidize aqueous-phase ammonia (NH3). Whereas as-synthesized Nb2O5 showed little catalytic activity (< 1% NH3 conversion after 6 h of UV-C irradiation, at room temperature and atmospheric pressure, and under O2 headspace), Nb2O5 treated in basic solution (OH-Nb2O5) was able to photocatalytically degrade NH3 at ca. 9% conversion after six hours, with ca. 70% selectivity to the desired N2, with a first-order rate constant of ca. 12 times higher than the as synthesize catalyst (1.6 × 10–3 min–1 vs. 2.0 × 10–2 min–1). Raman spectroscopic analysis indicated the presence of terminal Nb=O species after base treatment of Nb2O5, implicating them as catalytically active sites. These results underscore how a simple structural modification can significantly affect photocatalytic activity for aqueous ammonia oxidation.Item Atomically precise nanoclusters predominantly seed gold nanoparticle syntheses(Springer Nature, 2023) Qiao, Liang; Pollard, Nia; Senanayake, Ravithree D.; Yang, Zhi; Kim, Minjung; Ali, Arzeena S.; Hoang, Minh Tam; Yao, Nan; Han, Yimo; Hernandez, Rigoberto; Clayborne, Andre Z.; Jones, Matthew R.Seed-mediated synthesis strategies, in which small gold nanoparticle precursors are added to a growth solution to initiate heterogeneous nucleation, are among the most prevalent, simple, and productive methodologies for generating well-defined colloidal anisotropic nanostructures. However, the size, structure, and chemical properties of the seeds remain poorly understood, which partially explains the lack of mechanistic understanding of many particle growth reactions. Here, we identify the majority component in the seed solution as an atomically precise gold nanocluster, consisting of a 32-atom Au core with 8 halide ligands and 12 neutral ligands constituting a bound ion pair between a halide and the cationic surfactant: Au32X8[AQA+•X-]12 (X = Cl, Br; AQA = alkyl quaternary ammonium). Ligand exchange is dynamic and versatile, occurring on the order of minutes and allowing for the formation of 48 distinct Au32 clusters with AQAX (alkyl quaternary ammonium halide) ligands. Anisotropic nanoparticle syntheses seeded with solutions enriched in Au32X8[AQA+•X-]12 show narrower size distributions and fewer impurity particle shapes, indicating the importance of this cluster as a precursor to the growth of well-defined nanostructures.Item A deep learning solution for crystallographic structure determination(International Union of Crystallography, 2023) Pan, T.; Jin, S.; Miller, M. D.; Kyrillidis, A.; Phillips, G. N.The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.Item Graphene as a rational interface for enhanced adsorption of microcystin-LR from water(Elsevier, 2023) Roberts, Jesse L.; Zetterholm, Sarah Grace; Gurtowski, Luke; Fernando, PU Ashvin I.; Evans, Angela; Puhnaty, Justin; Wyss, Kevin M.; Tour, James M.; Fernando, Brianna; Jenness, Glen; Thompson, Audie; Griggs, Chris; Rice Advanced Materials Institute; Welch Institute for Advanced Materials; Smalley-Curl InstituteCyanotoxins such as microcystin-LR (MC-LR) represent a global environmental threat to ecosystems and drinking water supplies. The study investigated the direct use of graphene as a rational interface for removal of MC-LR via interactions with the aromatic ring of the ADDA1 chain of MC-LR and the sp2 hybridized carbon network of graphene. Intra-particle diffusion model fit indicated the high mesoporosity of graphene provided significant enhancements to both adsorption capacities and kinetics when benchmarked against microporous granular activated carbon (GAC). Graphene showed superior MC-LR adsorption capacity of 75.4 mg/g (Freundlich model) compared to 0.982 mg/g (Langmuir model) for GAC. Sorption kinetic studies showed graphene adsorbs 99% of MC-LR in 30 min, compared to zero removal for GAC after 24 hr using the same MC-LR concentration. Density functional theory (DFT), calculations showed that postulated π-based interactions align well with the NMR-based experimental work used to probe primary interactions between graphene and MC-LR adduct. This study proved that π-interactions between the aromatic ring on MC-LR and graphene sp2 orbitals are a dominant interaction. With rapid kinetics and adsorption capacities much higher than GAC, it is anticipated that graphene will offer a novel molecular approach for removal of toxins and emerging contaminants with aromatic systems.Item Mass Spectrometry of RNA-Binding Proteins during Liquid–Liquid Phase Separation Reveals Distinct Assembly Mechanisms and Droplet Architectures(American Chemical Society, 2023) Sahin, Cagla; Motso, Aikaterini; Gu, Xinyu; Feyrer, Hannes; Lama, Dilraj; Arndt, Tina; Rising, Anna; Gese, Genis Valentin; Hällberg, B. Martin; Marklund, Erik. G.; Schafer, Nicholas P.; Petzold, Katja; Teilum, Kaare; Wolynes, Peter G.; Landreh, Michael; Center for Theoretical Biological PhysicsLiquid–liquid phase separation (LLPS) of heterogeneous ribonucleoproteins (hnRNPs) drives the formation of membraneless organelles, but structural information about their assembled states is still lacking. Here, we address this challenge through a combination of protein engineering, native ion mobility mass spectrometry, and molecular dynamics simulations. We used an LLPS-compatible spider silk domain and pH changes to control the self-assembly of the hnRNPs FUS, TDP-43, and hCPEB3, which are implicated in neurodegeneration, cancer, and memory storage. By releasing the proteins inside the mass spectrometer from their native assemblies, we could monitor conformational changes associated with liquid–liquid phase separation. We find that FUS monomers undergo an unfolded-to-globular transition, whereas TDP-43 oligomerizes into partially disordered dimers and trimers. hCPEB3, on the other hand, remains fully disordered with a preference for fibrillar aggregation over LLPS. The divergent assembly mechanisms revealed by ion mobility mass spectrometry of soluble protein species that exist under LLPS conditions suggest structurally distinct complexes inside liquid droplets that may impact RNA processing and translation depending on biological context.Item The role of graphene in new thermoelectric materials(Royal Society of Chemistry, 2023) Mulla, Rafiq; White, Alvin Orbaek; Dunnill, Charles W.; Barron, Andrew R.Graphene has high electrical conductivity, making it an attractive material for thermoelectric applications. However, its high thermal conductivity is a major challenge, and initial studies indicate that using pristine graphene alone cannot achieve optimal thermoelectric performance. Therefore, researchers are exploring ways to improve thermoelectric materials by either leveraging graphene's high intrinsic electrical conductivity or compounding graphene with additives to reduce the intrinsic thermal conductivity of the materials. Therefore, the research focus is now being shifted to graphene composites, primarily with polymer/organic conductors. One promising avenue of research is the development of graphene composites with polymer or organic conductors, which have shown some improvements in thermoelectric performance. However, the achieved “dimensionless figure of merit (ZT)” values of these composites are still far lower than those of common inorganic semiconductors. An alternative approach involves incorporating a very small amount of graphene into inorganic materials to improve their overall thermoelectric properties. These new concepts have successfully addressed the detrimental effects of graphene's intrinsic thermal conductivity, with the added interfaces in the matrix due to the presence of graphene layers working to enhance the properties of the host material. The use of graphene presents a promising solution to the longstanding challenge of developing high-performance and cost-effective thermoelectric materials. This paper discusses these innovative research ideas, highlighting their potential for revolutionizing the field of thermoelectric materials.Item Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning(American Physical Society, 2023) Erdman, Paolo A.; Rolandi, Alberto; Abiuso, Paolo; Perarnau-Llobet, Martí; Noé, FrankThe full optimization of a quantum heat engine requires operating at high power, high efficiency, and high stability (i.e., low power fluctuations). However, these three objectives cannot be simultaneously optimized—as indicated by the so-called thermodynamic uncertainty relations—and a systematic approach to finding optimal balances between them including power fluctuations has, as yet, been elusive. Here we propose such a general framework to identify Pareto-optimal cycles for driven quantum heat engines that trade off power, efficiency, and fluctuations. We then employ reinforcement learning to identify the Pareto front of a quantum dot-based engine and find abrupt changes in the form of optimal cycles when switching between optimizing two and three objectives. We further derive analytical results in the fast- and slow-driving regimes that accurately describe different regions of the Pareto front.Item The oxido clusters of bismuth(Elsevier, 2023) Whitmire, Kenton H.; Wall, KathrynThe oxido clusters of bismuth have received increasing attention over the past several decades. This can in part be attributed to the use of bismuth compounds as pharmaceuticals, dating back hundreds of years for treating gastrointestinal disorders and as general antimicrobial and antifungal agents. This is enabled by bismuth's low toxicity compared to its more infamous neighbors in the periodic table. The field received a substantial boost in activity aimed at producing precursor compounds to homo- and heterometallic metal oxides via sol gel syntheses thanks to the discovery of various high TC superconducting bismuth-containing phases. More recently these compounds have shown interesting catalytic, electronic and optical properties. There is a strong resemblance of the structures of the oxido species to solid state bismuth oxides, and the chemistry of the oxido clusters is strongly intertwined with the fundamental coordination chemistry of bismuth, which shows strong Lewis acidity and high coordination numbers. As a result, the oxido clusters of bismuth have proven to have a rich structural chemistry with nuclearities ranging from two to fifty bismuth atoms, and many structures are known that incorporate other metals including the alkali metals, alkaline earth metals, transition metals and lanthanides as part of the oxido core. Compounds with a [Bi6O4(OH)4]6+ core are particularly prevalent, but Bi4, Bi9 and Bi38 structures are also well-represented. The ligand sets that support the formation and stabilization of these structures include a wide variety of organic alkoxides, siloxides, carboxylates, sulfonates, phosphonates and acetylacetonates. Multifunctional ligands have been used for the production of MOF-like structures via hydrothermal methods that can result in discrete bismuth oxido clusters linked via ligand bridges or polymeric bismuth oxido structures decorated with the organic ligands and organized into 1-, 2- or 3-dimensional structures. This review is designed to summarize the synthetic methods used to prepare them, present key aspects of their physical and chemical properties as well as discussing the important features of the structures reported to date. Of significant importance is the obvous blurring of the boundaries between the discrete bismuth oxido clusters that can be described as conventional inorganic coordination compounds, organobismuth oxides and oxido bismuth compounds where the bismuth oxido core is itself a polymer.Item State Preparation of Antisymmetrized Geminal Power on a Quantum Computer without Number Projection(American Chemical Society, 2023) Khamoshi, Armin; Dutta, Rishab; Scuseria, Gustavo E.The antisymmetrized geminal power (AGP) is equivalent to the number projected Bardeen–Cooper–Schrieffer (PBCS) wave function. It is also an elementary symmetric polynomial (ESP) state. We generalize previous research on deterministically implementing the Dicke state to a state preparation algorithm for an ESP state, or equivalently AGP, on a quantum computer. Our method is deterministic and has polynomial cost, and it does not rely on number symmetry breaking and restoration. We also show that our circuit is equivalent to a disentangled unitary paired coupled cluster operator and a layer of unitary Jastrow operator acting on a single Slater determinant. The method presented herein highlights the ability of disentangled unitary coupled cluster to capture nontrivial entanglement properties that are hardly accessible with traditional Hartree–Fock based electronic structure methods.