We present a complete theoretical characterization of Latent Posterior Factors (LPF), a principled framework for aggregating multiple heterogeneous evidence items in probabilistic prediction tasks. Multi-evidence reasoning arises pervasively in high-stakes domains including healthcare diagnosis, financial risk assessment, legal case analysis, and regulatory compliance, yet existing approaches either lack formal guarantees or fail to handle multi-evidence scenarios architecturally. LPF encodes each evidence item into a Gaussian latent posterior via a variational autoencoder, converting posteriors to soft factors through Monte Carlo marginalization, and aggregating factors via exact Sum-Product Network inference (LPF-SPN) or a learned neural aggregator (LPF-Learned). We prove seven formal guarantees spanning the key desiderata for trustworthy AI: Calibration Preservation (ECE <= epsilon + C/sqrt(K_eff)); Monte Carlo Error decaying as O(1/sqrt(M)); a non-vacuous PAC-Bayes bound with train-test gap of 0.0085 at N=4200; operation within 1.12x of the information-theoretic lower bound; graceful degradation as O(epsilon*delta*sqrt(K)) under corruption, maintaining 88% performance with half of evidence adversarially replaced; O(1/sqrt(K)) calibration decay with R^2=0.849; and exact epistemic-aleatoric uncertainty decomposition with error below 0.002%. All theorems are empirically validated on controlled datasets spanning up to 4,200 training examples. Our theoretical framework establishes LPF as a foundation for trustworthy multi-evidence AI in safety-critical applications.
Faisal Shah Khan, Norbert M. Linke, Anton Trong Than
et al.
Quantum games, like quantum algorithms, exploit quantum entanglement to establish strong correlations between strategic player actions. This paper introduces quantum game-theoretic models applied to trading and demonstrates their implementation on an ion-trap quantum computer. The results showcase a quantum advantage, previously known only theoretically, realized as higher-paying market Nash equilibria. This advantage could help uncover alpha in trading strategies, defined as excess returns compared to established benchmarks. These findings suggest that quantum computing could significantly influence the development of financial strategies.
Edgar P. Vidal, Alexander T. Gagliano, Carolina Cuesta-Lazaro
Time domain surveys such as the Vera C. Rubin Observatory are projected to annually discover millions of astronomical transients. This and complementary programs demand fast, automated methods to constrain the physical properties of the most interesting objects for spectroscopic follow up. Traditional approaches to likelihood-based inference are computationally expensive and ignore the multi-component energy sources powering astrophysical phenomena. In this work, we present a hierarchical simulation-based inference model for multi-band light curves that 1) identifies the energy sources powering an event of interest, 2) infers the physical properties of each subclass, and 3) separates physical anomalies in the learned embedding space. Our architecture consists of a transformer-based light curve summarizer coupled to a flow-matching regression module and a categorical classifier for the physical components. We train and test our model on $\sim$150k synthetic light curves generated with $\texttt{MOSFiT}$. Our network achieves a 90% classification accuracy at identifying energy sources, yields well-calibrated posteriors for all active components, and detects rare anomalies such as tidal disruption events (TDEs) through the learned latent space. This work demonstrates a scalable joint framework for population studies of known transients and the discovery of novel populations in the era of Rubin.
Priyankush Ghosh, Sambit Mishra, Shubham Dey
et al.
The launch of the James Webb Space Telescope (JWST) has delivered high-quality atmospheric observations and expanded the known chemical inventory of exoplanetary atmospheres, opening new avenues for atmospheric chemistry modeling to interpret these data. Here, we present XODIAC, a fast, GPU-accelerated, one-dimensional photochemical model with a built-in equilibrium chemistry solver, an updated thermochemical database, and three chemical reaction networks. This framework enables comparative atmospheric chemistry studies, including the newly developed XODIAC-2025 network, a state-of-the-art C-H-O-N-P-S-Metals network, linking 594 species through 7,720 reactions. The other two are existing, publicly available C-H-O-N-S and C-H-O-N-S-Metals networks, from the established photochemical models VULCAN and ARGO, respectively, which are commonly used in the community. The XODIAC model has been rigorously benchmarked on the well-studied hot Jupiter HD 189733 b, with results compared against these two models. Benchmarking shows excellent agreement and demonstrates that, when the same chemical network and initial conditions are used, the numerical scheme for solving atmospheric chemistry does not significantly affect the results. We also revisited the atmospheric chemistry of HD 189733 b and performed a comparative analysis across the three networks. Sulfur chemistry shows the least variation across networks, carbon chemistry shows slightly more, and phosphorus chemistry varies the most, primarily due to the introduction of unique PHO and PN pathways comprising 390 reactions in the XODIAC-2025 network. These findings highlight XODIAC's capability to advance exoplanetary atmospheric chemistry and provide a robust framework for comparative exoplanetology.
As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of $10^7-10^{15}$ for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools.
Hugh Morison, Jagmeet Singh, Nayem Al Kayed
et al.
Advances in silicon photonics technology have enabled the field of neuromorphic photonics, where analog neuron-like processing elements are implemented in silicon photonics technology. Accurate and scalable simulation tools for photonic integrated circuits are critical for designing neuromorphic photonic circuits. This is especially important when designing networks with recurrent connections, where the dynamics of the system may give rise to unstable and oscillatory solutions which need to be accurately modelled. These tools must simultaneously simulate the analog electronics and the multi-channel (wavelength-division-multiplexed) photonics contained in a photonic neuron to accurately predict on-chip behaviour. In this paper, we utilize a Verilog-A model of the photonic neural network to investigate the dynamics of recurrent integrated circuits. We begin by reviewing the theory of continuous-time recurrent neural networks as dynamical systems and the relation of these dynamics to important physical features of photonic neurons such as cascadability. We then present the neural dynamics of systems of one and two neurons in the simulated Verilog-A circuit, which are compared to the expected dynamics of the abstract CTRNN model. Due to the presence of parasitic circuit elements in the Verilog-A simulation, it is seen that there is a topological equivalence, but not an exact isomorphism, between the theoretical model and the simulated model. The implications of these discrepancies for the design of neuromorphic photonic circuits are discussed. Our findings pave the way for the practical implementation of large-scale silicon photonic recurrent neural networks.
Hossam Mohammed Abd El-Aziz, Mohamed A. Zayed, Soha Ali Abdel-Gawad
In this regard, green preparation of nanoscale-zero-valent copper was accomplished using Ficus benjamina leaves successfully. Ficus nano zero valent copper (Ficus-nZVCu) was characterized utilized scanning electron microscopy, and Fourier transform infrared spectroscopy. The results revealed that 63% of DR31 dye and 56% of DO26 dye were removed within the optimum conditions (0.3 g L -1 , 30 min, 50 mg L -1 , and pH 6). A Langmuir model was found to be the most fitting system for the removal process for DR31 and DO26. A Pseudo-second order kinetics model is the fit model of the removal processes for DR31 and DO26. The regenerated adsorbent still retained good adsorption capability after the fifth round of recycles. Ficus-nZVCu adsorbent is intensely recommended as a confident material for removing DR31 and DO26 anionic dyes from both prepared and actual wastewater samples. The co-presence of DR31 and DO26 did not affect each other's.
Fayet, Océane Yvonne Odette, Crespi, Stefano, Orthaber, Andreas
A series of solid-state structures of gold(I) complexes using the semi-rigid cis-1,2- bis(diphenylphosphino)ethene (= cis-bdppe) ligand are reported. The proximity of the phosphine donor atoms of the bidentate ligand framework greatly favors formation of mononuclear over di- nuclear complexes. Structural analysis and theoretical studies shed further light on counter intermolecular packing motifs in the solid state including interactions of solvent, ligand and counterion fragments.
Solving chemistry is an integral part of reacting flow simulations, usually dominating the computational cost. Among the different strategies to accelerate the solution of chemistry and to achieve realizable simulations, the use of Dynamic Adaptive Chemistry (DAC) stands out among other methods. DAC methods are based on the use of reduced mechanisms generated from local conditions. The reduction process is computationally expensive and strategies for reducing the frequency of reduction and the re-utilization of the generated reduced mechanisms are key in making DAC methods computationally affordable. In this study, a new method hereby referred as Tabulated Reactions for Adaptive Chemistry (TRAC) is proposed to correlate chemical states with their reduced mechanisms in order to reduce both the frequency of reduction and to allow for re-utilization of reduced chemical schemes. TRAC introduces a mechanism tabulation strategy based on the use of a low-dimensional space that defines the thermo-chemical conditions for which specific reduced reaction mechanisms are stored. Chemistry reduction is achieved by the use of Path Flux Analysis (PFA) with a reaction rate-sensitivity method to achieve further reduction in the reaction mechanisms. The new TRAC proposal is applied to various canonical transient problems and the results are compared with reference solutions obtained from detailed chemistry calculations. A speedup of about 4x was achieved with TRAC while maintaining an error under 3 % in the prediction of the major and minor species, flame structure, and flame propagation.
The enhanced degradation of organophosphorous-based chemical warfare agents (CWAs) on metal-oxide surfaces holds immense promise for neutralization efforts; however, the underlying mechanisms in this process remain poorly understood. We utilize large-scale quantum calculations for the first time to probe the high-temperature degradation of diisopropyl methylphosphonate (DIMP), a nerve agent simulant. Our Born-Oppenheimer molecular dynamics (BOMD) calculations show that the $γ$-Al$_2$O$_3$ surface shows immense promise for quickly adsorbing and destroying CWAs. We find that the alumina surface quickly adsorbs DIMP at all temperatures, and subsequent decomposition of DIMP proceeds via a propene elimination. Our BOMD calculations are complemented with metadynamics simulations to produce free energy paths, which show that the activation barrier decreases with temperature and DIMP readily decomposes on $γ$-Al$_2$O$_3$. Our first-principle BOMD and metadynamics simulations provide crucial diagnostics for sarin decomposition models and mechanistic information for examining CWA decomposition reactions on other candidate metal oxide surfaces.
Prussian Blue analogs (PBAs) are well-known coordination polymers offering a wide range of properties, and their fundamental understanding requires their investigations by various techniques. One of them is transition metal (TM) K-edge X-ray Magnetic Circular Dichroism (XMCD), which is element selective, bulk sensitive and compatible with a wide range of experimental conditions. This short review presents the reciprocal investigation of PBA with TM K-edge XMCD, from the first studies demonstrating that qualitative local magnetic information could be obtained on PBA from TM K-edge XMCD until our on-going project aiming at fundamentally understanding TM K-edge XMCD thanks to PBAs as model-compounds.
Conformational isomers or conformers of molecules play a decisive role in chemistry and biology. However, experimental methods to investigate chemical reaction dynamics are typically not conformer-sensitive. Here, we report on a gas-phase megaelectronvolt ultrafast electron diffraction investigation of α-phellandrene undergoing an electrocyclic ring-opening reaction. We directly image the evolution of a specific set of α-phellandrene conformers into the product isomer predicted by the Woodward-Hoffmann rules in real space and time. Our experimental results are in quantitative agreement with nonadiabatic quantum molecular dynamics simulations, which provide unprecedented detail of how conformation influences time scale and quantum efficiency of photoinduced ring-opening reactions. Due to the prevalence of large numbers of conformers in organic chemistry, our findings impact our general understanding of reaction dynamics in chemistry and biology.
Perovskites are among the most famous materials due to their exceptional properties: they present nearly all existing types of interesting properties, in particular as ferroics or multiferroics, they may be insulators, (super)conductors, or semiconductors, magnetoresistant, they are used in numerous devices, they present hundreds of variants and different crystalline phases and phase transitions, and recently appeared as probably the most promising materials for photovoltaics. With a crystal structure characterized by octahedra that share their corners, these materials belong to the wider category of « Framework Structure (FWS) materials » the structure of which is based on units (octahedra, tetrahedra, …) that share some of their corners (or edges) with their neighbours. This particular feature of FWS materials confers to them unique properties. This review volume is constituted of 26 chapters on different aspects, and is divided in two parts, « Fundamental aspects and general properties », and « Elaborated materials and applied properties ». Its main purpose is to attempt to identify the properties common to all members of the vast family of FWS materials, and understand their differences. Besides perovskites, derived compounds as 2D perovskites, Dion-Jacobson, Ruddlesden-Popper, Aurivillius, tungsten-bronzes, and others, are presented, and their preparation and/or properties as single crystals, ceramics, thin films, multilayers, nanomaterials, nanofibers, nanorods, etc, are discussed. We focus on new trends and important recent developments by leaving somewhat aside more classical aspects which can be easily found in older textbooks or review articles. Among most recent applications, this volume focuses on applications related with interactions with other molecules, on photovoltaics, and on memories, with a special attention to perovskite solar cells that have certainly attracted the most attention of researchers in recent years, opening extremely promising routes in photovoltaics.
In conclusion, this book presents a collection of texts elucidating various aspects of the relation between structural organization (including dynamical aspects) and singular properties of framework crystals; it proposes a reasonable balance between experimental and theoretical results, and between fundamental aspects and applied properties.
This volume can be approached on several levels (each chapter initially assumes that the reader is not a specialist in the subject, and is presented in a pedagogical way) : it is accessible to master or doctoral students, as well as to researchers who want to have informations on recent developments, who will find excellent detailed introductions up to hotsubjects. It may also be used by undergraduate students who should approach given subjects. The volume contains 800 pages written by about 70 authors from different countries, it has an index, and is completed by numerous figures to illustrate the text.
Crystallography, Physical and theoretical chemistry
Vincenzo Bonifaci, Enrico Facca, Frederic Folz
et al.
In wet-lab experiments, the slime mold Physarum polycephalum has demonstrated its ability to solve shortest path problems and to design efficient networks. For the shortest path problem, a mathematical model for the evolution of the slime is available and it has been shown in computer experiments and through mathematical analysis that the dynamics solves the shortest path problem. In this paper, we introduce a dynamics for the network design problem. We formulate network design as the problem of constructing a network that efficiently supports a multi-commodity flow problem. We investigate the dynamics in computer simulations and analytically. The simulations show that the dynamics is able to construct efficient and elegant networks. In the theoretical part we show that the dynamics minimizes an objective combining the cost of the network and the cost of routing the demands through the network. We also give alternative characterization of the optimum solution.