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Metadata practices for simulation workflows
No full textComputer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking and organizing the metadata describing the numerical experiments. The models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, fostering replicability, reproducibility, data exploration, and data sharing in simulation-based research
Methodological insights of defining material criticality by assessing different electrolysis and fuel cell stacks
No full textImplementation of ion exclusion chromatography for characterization of lithium ion battery materials
No full textInorganic compounds such as lithium fluoride (LiF) and lithium carbonate (Li2CO3) as well as weakly acidic lithium salts like lithium acetate (LiCH3CO2) or lithium formate (LiHCO2) are reported decomposition products in lithium ion batteries (LIBs). The simultaneous analysis of these compounds is challenging due to the complex system consisting of conductive salt, organic carbonates, additives and their decomposition variety. Ion exclusion chromatography with conductivity detection (IEC-CD) seems to be predestinated for this analytical task due to its ability to separate and determine weakly acidic anions, which are the relevant species arising from lithium salts and electrolyte decomposition processes. One important chromatographic method to analyze ionic decomposition products is ion exchange chromatography (IC), which is currently a state-of-the-art (SOTA) technique for fluoride (F-) quantification in LIBs. However, the calibration curve of F- by IC hyphenated to a conductivity detection (CD) provides a small linear range for low concentrations and an analyte dependent retention shift occurs. IEC-CD represents a substantial upgrade in this respect and generated benefits for electrolyte analysis by an improved linear range for F- (up to several 100 ppm). Furthermore, especially in complex samples, the IEC-CD method provides a more reliable chromatographic separation. In this study, IEC-CD is implemented to investigate decomposition pathways of fluor-releasing electrolyte additives such as fluoroethylene carbonate (FEC). The quantification of formate (HCO2-), acetate (CH3CO2-) and carbonate (CO32-) was also possible to gain deeper understanding of electrolyte additive decomposition in LIBs
The Quantum Optical Master Equation is of the same order of approximation as the Redfield Equation
No full textQuantum master equations are widely used to describe the dynamics of open quantum systems. All these different master equations rely on specific approximations that may or may not be justified. Starting from a microscopic model, applying the justified approximations only may not result in the desired Lindblad form preserving positivity. The recently proposed Universal Lindblad Equation is in Lindblad form and still retains the same order of approximation as the Redfield master equation [arXiv:2004.01469]. In this work, we prove that the well-known Quantum Optical Master Equation is also in the same equivalence class of approximations. We furthermore compare the Quantum Optical Master Equation and the Universal Lindblad Equation numerically and show numerical evidence that the Quantum Optical Master Equation yields more accurate results
Optimizing QAOA circuit transpilation with parity twine and SWAP network encodings
No full textMapping quantum approximate optimization algorithm (QAOA) circuits with non-trivial connectivity in fixed-layout quantum platforms such as superconducting-based quantum processing units (QPUs) requires a process of transpilation to match the quantum circuit on the given layout. This step is critical for reducing error rates when running on noisy QPUs. Two methodologies that improve the resource required to do such transpilation are the SWAP network and parity twine chains (PTC). These approaches reduce the two-qubit gate count and depth needed to represent fully connected circuits. In this work, a simulated annealing-based method is introduced that reduces the PTC and SWAP network encoding requirements in QAOA circuits with non-fully connected two-qubit gates. This method is benchmarked against various transpilers and demonstrates that, beyond specific connectivity thresholds, it achieves significant reductions in both two-qubit gate count and circuit depth, surpassing the performance of Qiskit transpiler at its highest optimization level. For example, for a 120-qubit QAOA instance with 25% connectivity, our method achieves an 85% reduction in depth and a 28% reduction in two-qubit gates. Finally, the practical impact of PTC encoding is validated by benchmarking QAOA on the ibm_fez device, showing improved performance up to 20 qubits, compared to a 15-qubit limit when using SWAP networks
Tracing the aggregation pathway of the scaffold protein DISC1: Structural implications for chronic mental illnesses
No full textDisrupted in schizophrenia 1 (DISC1) is a pleiotropic scaffold protein that is postulated to comprise large disordered regions and four distinct structured segments with a high proportion of helical or coiled-coil fold. DISC1 associates with over 300 proteins and is associated with several physiological roles ranging from mitosis to cellular differentiation. Yet, the structural features of the protein are poorly characterized. The C-terminal region (C-region, res. 691–836) forms a tetramer and can also aggregate into amyloid-like fibers, potentially linked to schizophrenia and other chronic mental illnesses. Using a combination of biophysical and structural biology applications, we investigate the structural heterogeneity of three mutants of the C-region, viz., the S713E, S704C and L807-frameshift mutants. We provide evidence for the plasticity of the C region; a thin border separates the conformational flexibility of DISC1 required for interaction with a myriad of partners from disruptive aggregation. Snapshots of aggregates and fibrils growing from a nucleus are presented, along with data supporting the role of the minimal fibrillizing element in the C-region, the β-core. This segment also houses a stretch of residues that is critical for the binding of NDEL1 proteins in the mitotic spindle complex and is absent in the non-binding splice variant DISC1Δ22aa. Physiologically, both the splice variant and the fibers represent loss-of-function states that disrupt cellular division. Our findings highlight the need to decipher the structural elements within the DISC1 C-region to comprehend its physiological role and aggregation-related anomalies, and to establish a rationale for drug development