@article{18952, abstract = {A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.}, author = {Hunnisett, Lily M. and Nyman, Jonas and Francia, Nicholas and Abraham, Nathan S. and Adjiman, Claire S. and Aitipamula, Srinivasulu and Alkhidir, Tamador and Almehairbi, Mubarak and Anelli, Andrea and Anstine, Dylan M. and Anthony, John E. and Arnold, Joseph E. and Bahrami, Faezeh and Bellucci, Michael A. and Bhardwaj, Rajni M. and Bier, Imanuel and Bis, Joanna A. and Boese, A. Daniel and Bowskill, David H. and Bramley, James and Brandenburg, Jan Gerit and Braun, Doris E. and Butler, Patrick W. V. and Cadden, Joseph and Carino, Stephen and Chan, Eric J. and Chang, Chao and Cheng, Bingqing and Clarke, Sarah M. and Coles, Simon J. and Cooper, Richard I. and Couch, Ricky and Cuadrado, Ramon and Darden, Tom and Day, Graeme M. and Dietrich, Hanno and Ding, Yiming and DiPasquale, Antonio and Dhokale, Bhausaheb and van Eijck, Bouke P. and Elsegood, Mark R. J. and Firaha, Dzmitry and Fu, Wenbo and Fukuzawa, Kaori and Glover, Joseph and Goto, Hitoshi and Greenwell, Chandler and Guo, Rui and Harter, Jürgen and Helfferich, Julian and Hofmann, Detlef W. M. and Hoja, Johannes and Hone, John and Hong, Richard and Hutchison, Geoffrey and Ikabata, Yasuhiro and Isayev, Olexandr and Ishaque, Ommair and Jain, Varsha and Jin, Yingdi and Jing, Aling and Johnson, Erin R. and Jones, Ian and Jose, K. V. Jovan and Kabova, Elena A. and Keates, Adam and Kelly, Paul F. and Khakimov, Dmitry and Konstantinopoulos, Stefanos and Kuleshova, Liudmila N. and Li, He and Lin, Xiaolu and List, Alexander and Liu, Congcong and Liu, Yifei Michelle and Liu, Zenghui and Liu, Zhi-Pan and Lubach, Joseph W. and Marom, Noa and Maryewski, Alexander A. and Matsui, Hiroyuki and Mattei, Alessandra and Mayo, R. Alex and Melkumov, John W. and Mohamed, Sharmarke and Momenzadeh Abardeh, Zahrasadat and Muddana, Hari S. and Nakayama, Naofumi and Nayal, Kamal Singh and Neumann, Marcus A. and Nikhar, Rahul and Obata, Shigeaki and O'Connor, Dana and Oganov, Artem R. and Okuwaki, Koji and Otero-de-la-Roza, Alberto and Pantelides, Constantinos C. and Parkin, Sean and Pickard, Chris J. and Pilia, Luca and Pivina, Tatyana and Podeszwa, Rafał and Price, Alastair J. A. and Price, Louise S. and Price, Sarah L. and Probert, Michael R. and Pulido, Angeles and Ramteke, Gunjan Rajendra and Rehman, Atta Ur and Reutzel-Edens, Susan M. and Rogal, Jutta and Ross, Marta J. and Rumson, Adrian F. and Sadiq, Ghazala and Saeed, Zeinab M. and Salimi, Alireza and Salvalaglio, Matteo and Sanders de Almada, Leticia and Sasikumar, Kiran and Sekharan, Sivakumar and Shang, Cheng and Shankland, Kenneth and Shinohara, Kotaro and Shi, Baimei and Shi, Xuekun and Skillman, A. Geoffrey and Song, Hongxing and Strasser, Nina and van de Streek, Jacco and Sugden, Isaac J. and Sun, Guangxu and Szalewicz, Krzysztof and Tan, Benjamin I. and Tan, Lu and Tarczynski, Frank and Taylor, Christopher R. and Tkatchenko, Alexandre and Tom, Rithwik and Tuckerman, Mark E. and Utsumi, Yohei and Vogt-Maranto, Leslie and Weatherston, Jake and Wilkinson, Luke J. and Willacy, Robert D. and Wojtas, Lukasz and Woollam, Grahame R. and Yang, Zhuocen and Yonemochi, Etsuo and Yue, Xin and Zeng, Qun and Zhang, Yizu and Zhou, Tian and Zhou, Yunfei and Zubatyuk, Roman and Cole, Jason C.}, issn = {2052-5206}, journal = {Acta Crystallographica Section B}, number = {6}, pages = {517--547}, publisher = {International Union of Crystallography}, title = {{The seventh blind test of crystal structure prediction: Structure generation methods}}, doi = {10.1107/s2052520624007492}, volume = {80}, year = {2024}, } @article{15359, abstract = {Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.}, author = {Dong, Haikuan and Shi, Yongbo and Ying, Penghua and Xu, Ke and Liang, Ting and Wang, Yanzhou and Zeng, Zezhu and Wu, Xin and Zhou, Wenjiang and Xiong, Shiyun and Chen, Shunda and Fan, Zheyong}, issn = {1089-7550}, journal = {Journal of Applied Physics}, number = {16}, publisher = {AIP Publishing}, title = {{Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials}}, doi = {10.1063/5.0200833}, volume = {135}, year = {2024}, } @article{18452, abstract = {Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus analogous to the geometry relaxation of atomistic systems starting from a random structure. Building on this, we present a generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching this response is closely related to score matching in diffusion models. Another important aspect of state-of-the-art diffusion models is the incorporation of physical symmetries such as translation, rotation, and periodicity. RM employs a machine learning interatomic potential and random structure search as the denoising model, inherently respecting these symmetries and exploiting the locality of atomic interactions. RM handles both molecules and bulk materials under the same framework. Its efficiency and generalization are demonstrated on three systems: a small organic molecular data set, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.}, author = {Cheng, Bingqing}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {20}, pages = {9259--9266}, publisher = {American Chemical Society}, title = {{Response matching for generating materials and molecules}}, doi = {10.1021/acs.jctc.4c00998}, volume = {20}, year = {2024}, } @article{17322, abstract = {Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, followed by Clebsch-Gordan contraction to maintain rotational symmetry. We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates. This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements, trainable radial channel coupling, and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys.}, author = {Cheng, Bingqing}, issn = {2057-3960}, journal = {npj Computational Materials}, publisher = {Springer Nature}, title = {{Cartesian atomic cluster expansion for machine learning interatomic potentials}}, doi = {10.1038/s41524-024-01332-4}, volume = {10}, year = {2024}, } @article{17278, abstract = {An azeotrope is a constant boiling point mixture, and its behavior is important for fluid separation processes. Predicting azeotropes from atomistic simulations is difficult due to the complexities and convergence problems of Monte Carlo and free-energy perturbation techniques. Here, we present a methodology for predicting the azeotropes of binary mixtures, which computes the compositional dependence of chemical potentials from molecular dynamics simulations using the S0 method and employs experimental boiling point and vaporization enthalpy data. Using this methodology, we reproduce the azeotropes, or lack thereof, in five case studies, including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and acetone/chloroform mixtures. We find that it is crucial to use the experimental boiling point and vaporization enthalpy for reliable azeotrope predictions, as empirical force fields are not accurate enough for these quantities. Finally, we use regular solution models to rationalize the azeotropes and reveal that they tend to form when the mixture components have similar boiling points and strong interactions.}, author = {Wang, Xiaoyu and Cheng, Bingqing}, issn = {1089-7690}, journal = {Journal of Chemical Physics}, number = {3}, publisher = {AIP Publishing}, title = {{Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures}}, doi = {10.1063/5.0217232}, volume = {161}, year = {2024}, } @article{18525, abstract = {As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the “quantitative omnigenic model” (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.}, author = {Ruzickova, Natalia and Hledik, Michal and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {44}, publisher = {National Academy of Sciences}, title = {{Quantitative omnigenic model discovers interpretable genome-wide associations}}, doi = {10.1073/pnas.2402340121}, volume = {121}, year = {2024}, } @article{17052, abstract = {Production of thermoelectric materials from solution-processed particles involves the synthesis of particles, their purification and densification into pelletized material. Chemical changes that occur during each one of these steps render them performance determining. Particularly the purification steps, bypassed in conventional solid-state synthesis, are the cause for large discrepancies among similar solution-processed materials. In present work, the investigation focuses on a water-based surfactant free solution synthesis of SnSe, a highly relevant thermoelectric material. We show and rationalize that the number of leaching steps, purification solvent, annealing, and annealing atmosphere have significant influence on the Sn : Se ratio and impurity content in the powder. Such compositional changes that are undetectable by conventional characterization techniques lead to distinct consolidated materials with different types and concentration of defects. Additionally, the profound effect on their transport properties is demonstrated. We emphasize that understanding the chemistry and identifying key chemical species and their role throughout the process is paramount for optimizing material performance. Furthermore, we aim to demonstrate the necessity of comprehensive reporting of these steps as a standard practice to ensure material reproducibility.}, author = {Fiedler, Christine and Calcabrini, Mariano and Liu, Yu and Ibáñez, Maria}, issn = {1521-3773}, journal = {Angewandte Chemie International Edition}, number = {25}, publisher = {Wiley}, title = {{Unveiling crucial chemical processing parameters influencing the performance of solution-processed inorganic thermoelectric materials}}, doi = {10.1002/anie.202402628}, volume = {63}, year = {2024}, } @article{17124, abstract = {In recent years, solution processes have gained considerable traction as a cost-effective and scalable method to produce high-performance thermoelectric materials. The process entails a series of critical steps: synthesis, purification, thermal treatments, and consolidation, each playing a pivotal role in determining performance, stability, and reproducibility. We have noticed a need for more comprehensive details for each of the described steps in most published works. Recognizing the significance of detailed synthetic protocols, we describe here the approach used to synthesize and characterize one of the highest-performing polycrystalline p-type SnSe. In particular, we report the synthesis of SnSe particles in water and the subsequent surface treatment with CdSe molecular complexes that yields CdSe-SnSe nanocomposites upon consolidation. Moreover, the surface treatment inhibits grain growth through Zenner pinning of secondary phase CdSe nanoparticles and enhances defect formation at different length scales. The enhanced complexity in the CdSe-SnSe nanocomposite microstructure with respect to SnSe promotes phonon scattering and thereby significantly reduces the thermal conductivity. Such surface engineering provides opportunities in solution processing for introducing and controlling defects, making it possible to optimize the transport properties and attain a high thermoelectric figure of merit.}, author = {Fiedler, Christine and Liu, Yu and Ibáñez, Maria}, issn = {1940-087X}, journal = {Journal of Visualized Experiments}, number = {207}, publisher = {MyJove Corporation}, title = {{Solution-processed, surface-engineered, polycrystalline CdSe-SnSe exhibiting low thermal conductivity}}, doi = {10.3791/66278}, volume = {2024}, year = {2024}, } @article{15298, abstract = {Mountains are important suppliers of freshwater to downstream areas, affecting large populations in particular in High Mountain Asia (HMA). Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the cryo-, hydro- and biosphere remain elusive. We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain (blue) and water lost to the atmosphere through vapor fluxes, that is evaporation from water, ground, and interception plus transpiration (green) and propose to add the term white water to account for the (often neglected) evaporation and sublimation from snow and ice. We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments, going beyond disciplinary separations. Land surface models are uniquely able to account for such complexity, since they solve the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. Due to the mechanistic nature of such models, specific variables can be compared systematically to independent remote sensing observations – providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA. We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions. We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results. We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA.}, author = {Buri, Pascal and Fatichi, Simone and Shaw, Thomas and Fyffe, Catriona Louise and Miles, Evan S. and Mccarthy, Michael and Kneib, Marin and Ren, Shaoting and Jouberton, Achille and Fugger, Stefan and Jia, Li and Zhang, Jing and Shen, Cong and Zheng, Chaolei and Menenti, Massimo and Pellicciotti, Francesca}, issn = {1009-5020}, journal = {Geo-Spatial Information Science}, number = {3}, pages = {703--727}, publisher = {Taylor & Francis}, title = {{Land surface modeling informed by earth observation data: Toward understanding blue–green–white water fluxes in High Mountain Asia}}, doi = {10.1080/10095020.2024.2330546}, volume = {27}, year = {2024}, } @article{17231, abstract = {In the class of projective billiards, which contains the usual billiards, we exhibit counter-examples to Ivrii's conjecture, which states that in any planar billiard with smooth boundary the set of periodic orbits has zero measure. The counter-examples are polygons admitting a 2-parameters family of n-periodic orbits, with n being either 3 or any even integer greater than 4.}, author = {Fiorebe, Corentin}, issn = {1553-5231}, journal = {Discrete and Continuous Dynamical Systems- Series A}, number = {11}, pages = {3287--3301}, publisher = {AIMS}, title = {{Examples of projective billiards with open sets of periodic orbits}}, doi = {10.3934/dcds.2024059}, volume = {44}, year = {2024}, } @unpublished{15091, abstract = {Motivated by applications in the medical sciences, we study finite chromatic sets in Euclidean space from a topological perspective. Based on the persistent homology for images, kernels and cokernels, we design provably stable homological quantifiers that describe the geometric micro- and macro-structure of how the color classes mingle. These can be efficiently computed using chromatic variants of Delaunay and alpha complexes, and code that does these computations is provided.}, author = {Cultrera di Montesano, Sebastiano and Draganov, Ondrej and Edelsbrunner, Herbert and Saghafian, Morteza}, booktitle = {arXiv}, title = {{Chromatic alpha complexes}}, doi = {10.48550/arXiv.2212.03128}, year = {2024}, } @inproceedings{17898, abstract = {There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convolutional to Vision Transformers, from small to gigantic networks, have been extensively tested on offline image classification tasks. In this paper, we study these vision models with respect to the open-loop training to closed-loop generalization abilities, i.e., deployment realizes a causal feedback loop that is not present during training. This causality gap typically emerges in robotics applications such as autonomous driving, where a network is trained to imitate the control commands of a human. In this setting, two situations arise: 1) Closed-loop testing in-distribution, where the test environment shares properties with those of offline training data. 2) Closed-loop testing under distribution shifts and out-of-distribution. Contrary to recently reported results, we show that under proper training guidelines, all vision architectures perform indistinguishably well on in-distribution deployment, resolving the causality gap. In situation 2, We observe that scale is the strongest factor in improving closed-loop generalization regardless of the choice of the model architecture. Our results predict the trend that in the future we will see larger and larger models being used in offline-training-online-deployment imitation learning tasks in robotic applications.}, author = {Lechner, Mathias and Hasani, Ramin and Amini, Alexander and Wang, Tsun Hsuan and Henzinger, Thomas A and Rus, Daniela}, booktitle = {Proceedings of the 2024 IEEE International Conference on Robotics and Automation}, isbn = {9798350384574}, issn = {1050-4729}, location = {Yokohama, Japan}, pages = {2774--2782}, publisher = {IEEE}, title = {{Overparametrization helps offline-to-online generalization of closed-loop control from pixels}}, doi = {10.1109/ICRA57147.2024.10610284}, year = {2024}, } @inproceedings{17893, abstract = {Strong data processing inequalities (SDPI) are an important object of study in Information Theory and have been well studied for f -divergences. Universal upper and lower bounds have been provided along with several applications, connecting them to impossibility (converse) results, concentration of measure, hypercontractivity, and so on. In this paper, we study Renyi divergence and the corresponding SDPI constant whose behavior seems to deviate from that of ordinary <1>-divergences. In particular, one can find examples showing that the universal upper bound relating its SDPI constant to the one of Total Variation does not hold in general. In this work, we prove, however, that the universal lower bound involving the SDPI constant of the Chi-square divergence does indeed hold. Furthermore, we also provide a characterization of the distribution that achieves the supremum when is equal to 2 and consequently compute the SDPI constant for Renyi divergence of the general binary channel.}, author = {Jin, Lifu and Esposito, Amedeo Roberto and Gastpar, Michael}, booktitle = {Proceedings of the 2024 IEEE International Symposium on Information Theory}, isbn = {9798350382846}, issn = {2157-8095}, location = {Athens, Greece}, pages = {3178--3183}, publisher = {IEEE}, title = {{Properties of the strong data processing constant for Rényi divergence}}, doi = {10.1109/ISIT57864.2024.10619367}, year = {2024}, } @inproceedings{17895, abstract = {We propose a concatenated code construction for a class of discrete-alphabet oblivious arbitrarily varying channels (AVCs) with cost constraints. The code has time and space complexity polynomial in the blocklength n . It uses a Reed-Solomon outer code, logarithmic blocklength random inner codes, and stochastic encoding by permuting the codeword before transmission. When the channel satisfies a condition called strong DS-nonsymmetrizability (a modified version of nonsymmetrizability originally due to Dobrushin and Stambler), we show that the code achieves a rate that for a variety of oblivious AVCs (such as classically studied error/erasure channels) match the known capacities.}, author = {Dey, B. K. and Jaggi, S. and Langberg, M. and Sarwate, A. D. and Zhang, Yihan}, booktitle = {Proceedings of the 2024 IEEE International Symposium on Information Theory }, isbn = {9798350382846}, issn = {2157-8095}, location = {Athens, Greece}, pages = {1586--1591}, publisher = {IEEE}, title = {{Computationally efficient codes for strongly Dobrushin-Stambler nonsymmetrizable oblivious AVCs}}, doi = {10.1109/ISIT57864.2024.10619362}, year = {2024}, } @inproceedings{17894, abstract = {Sibson's α -mutual information has received renewed attention recently in several contexts: concentration of measure under dependence, statistical learning, hypothesis testing, and estimation theory. In this work, we introduce several variational representations of Sibson's α -mutual information: 1) as a supremum over joint distributions of (a combination of) KL divergences; and 2) as a supremum over functions of opportune expected values. Leveraging them, we produce a variety of novel and known results, including a generalization of transportation-cost inequalities and Fano's inequality.}, author = {Esposito, Amedeo Roberto and Gastpar, Michael and Issa, Ibrahim}, booktitle = {Proceedings of the 2024 IEEE International Symposium on Information Theory }, isbn = {9798350382846}, issn = {2157-8095}, location = {Athens, Greece}, pages = {2110--2115}, publisher = {IEEE}, title = {{Variational characterizations of Sibson's α-mutual information}}, doi = {10.1109/ISIT57864.2024.10619378}, year = {2024}, } @article{18630, abstract = {Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decision processes (MDPs) extend Markov chains by incorporating non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization criterion is the maximal expected total reward where the MDP stops after T steps, which can be computed by a simple dynamic programming algorithm. We consider a natural generalization of the problem where the stopping times can be chosen according to a probability distribution, such that the expected stopping time is T, to optimize the expected total reward. Quite surprisingly we establish inter-reducibility of the expected stopping-time problem for Markov chains with the Positivity problem (which is related to the well-known Skolem problem), for which establishing either decidability or undecidability would be a major breakthrough. Given the hardness of the exact problem, we consider the approximate version of the problem: we show that it can be solved in exponential time for Markov chains and in exponential space for MDPs.}, author = {Chatterjee, Krishnendu and Doyen, Laurent}, issn = {1860-5974}, journal = {Logical Methods in Computer Science}, number = {4}, pages = {11:1--11:34}, publisher = {EPI Sciences}, title = {{Stochastic processes with expected stopping time}}, doi = {10.46298/lmcs-20(4:11)2024}, volume = {20}, year = {2024}, } @article{18923, abstract = {Combinatorial optimization is a challenging problem applicable in a wide range of fields from logistics to finance. Recently, quantum computing has been used to attempt to solve these problems using a range of algorithms, including parameterized quantum circuits, adiabatic protocols, and quantum annealing. These solutions typically have several challenges: 1) there is little to no performance gain over classical methods; 2) not all constraints and objectives may be efficiently encoded in the quantum ansatz; and 3) the solution domain of the objective function may not be the same as the bit strings of measurement outcomes. This work presents “nonnative hybrid algorithms”: a framework to overcome these challenges by integrating quantum and classical resources with a hybrid approach. By designing nonnative quantum variational anosatzes that inherit some but not all problem structure, measurement outcomes from the quantum computer can act as a resource to be used by classical routines to indirectly compute optimal solutions, partially overcoming the challenges of contemporary quantum optimization approaches. These methods are demonstrated using a publicly available neutral-atom quantum computer on two simple problems of Max k-Cut and maximum independent set. We find improvements in solution quality when comparing the hybrid algorithm to its “no quantum” version, a demonstration of a “comparative advantage.”}, author = {Wurtz, Jonathan and Sack, Stefan and Wang, Sheng-Tao}, issn = {2689-1808}, journal = {IEEE Transactions on Quantum Engineering}, pages = {1--14}, publisher = {IEEE}, title = {{Solving nonnative combinatorial optimization problems using hybrid quantum–classical algorithms}}, doi = {10.1109/tqe.2024.3443660}, volume = {5}, year = {2024}, } @article{17481, abstract = {Phase-field models such as the Allen–Cahn equation may give rise to the formation and evolution of geometric shapes, a phenomenon that may be analyzed rigorously in suitable scaling regimes. In its sharp-interface limit, the vectorial Allen–Cahn equation with a potential with N≥3 distinct minima has been conjectured to describe the evolution of branched interfaces by multiphase mean curvature flow. In the present work, we give a rigorous proof for this statement in two and three ambient dimensions and for a suitable class of potentials: as long as a strong solution to multiphase mean curvature flow exists, solutions to the vectorial Allen–Cahn equation with well-prepared initial data converge towards multiphase mean curvature flow in the limit of vanishing interface width parameter ε↘0. We even establish the rate of convergence O(ε 1/2 ). Our approach is based on the gradient-flow structure of the Allen–Cahn equation and its limiting motion: building on the recent concept of “gradient-flow calibrations” for multiphase mean curvature flow, we introduce a notion of relative entropy for the vectorial Allen–Cahn equation with multi-well potential. This enables us to overcome the limitations of other approaches, e.g. avoiding the need for a stability analysis of the Allen–Cahn operator or additional convergence hypotheses for the energy at positive times.}, author = {Fischer, Julian L and Marveggio, Alice}, issn = {1873-1430}, journal = {Annales de l'Institut Henri Poincaré C}, number = {5}, pages = {1117--1178}, publisher = {EMS Press}, title = {{Quantitative convergence of the vectorial Allen–Cahn equation towards multiphase mean curvature flow}}, doi = {10.4171/AIHPC/109}, volume = {41}, year = {2024}, } @article{15048, abstract = {Embryogenesis results from the coordinated activities of different signaling pathways controlling cell fate specification and morphogenesis. In vertebrate gastrulation, both Nodal and BMP signaling play key roles in germ layer specification and morphogenesis, yet their interplay to coordinate embryo patterning with morphogenesis is still insufficiently understood. Here, we took a reductionist approach using zebrafish embryonic explants to study the coordination of Nodal and BMP signaling for embryo patterning and morphogenesis. We show that Nodal signaling triggers explant elongation by inducing mesendodermal progenitors but also suppressing BMP signaling activity at the site of mesendoderm induction. Consistent with this, ectopic BMP signaling in the mesendoderm blocks cell alignment and oriented mesendoderm intercalations, key processes during explant elongation. Translating these ex vivo observations to the intact embryo showed that, similar to explants, Nodal signaling suppresses the effect of BMP signaling on cell intercalations in the dorsal domain, thus allowing robust embryonic axis elongation. These findings suggest a dual function of Nodal signaling in embryonic axis elongation by both inducing mesendoderm and suppressing BMP effects in the dorsal portion of the mesendoderm.}, author = {Schauer, Alexandra and Pranjic-Ferscha, Kornelija and Hauschild, Robert and Heisenberg, Carl-Philipp J}, issn = {1477-9129}, journal = {Development}, number = {4}, pages = {1--18}, publisher = {Company of Biologists}, title = {{Robust axis elongation by Nodal-dependent restriction of BMP signaling}}, doi = {10.1242/dev.202316}, volume = {151}, year = {2024}, } @article{17458, abstract = {Changes in gene dosage can have tremendous evolutionary potential (e.g. whole-genome duplications), but without compensatory mechanisms, they can also lead to gene dysregulation and pathologies. Sex chromosomes are a paradigmatic example of naturally occurring gene dosage differences and their compensation. In species with chromosome-based sex determination, individuals within the same population necessarily show ‘natural’ differences in gene dosage for the sex chromosomes. In this Review, we focus on the mammalian X chromosome and discuss recent new insights into the dosage-compensation mechanisms that evolved along with the emergence of sex chromosomes, namely X-inactivation and X-upregulation. We also discuss the evolution of the genetic loci and molecular players involved, as well as the regulatory diversity and potentially different requirements for dosage compensation across mammalian species.}, author = {Cecalev, Daniela and Vicoso, Beatriz and Galupa, Rafael}, issn = {1477-9129}, journal = {Development}, number = {15}, publisher = {Company of Biologists}, title = {{Compensation of gene dosage on the mammalian X}}, doi = {10.1242/dev.202891}, volume = {151}, year = {2024}, }