Computational Challenges Research Articles

Homogenized Modeling of CO2/CO Reduction Catalyst Microenvironments with Intermixed Hydrophobic and Hydrophilic Regions

Electroreduction of CO and CO2 offer a pathway toward renewable-powered synthesis of hydrocarbon chemicals and fuels. Both the composition and the structure of cathodic catalyst microenvironments in gas diffusion electrodes play a dominant role in determining the product composition and efficiency metrics. Porous and hydrophilic copper environments are known to produce multi-carbon species and exhibit large active surface areas, but recent investigations have shown that the inclusion of sporadically-distributed porous hydrophobic regions creates additional pathways for the delivery of gaseous reactants, thereby mitigating transport limitations seen in pure Cu catalysts.As the design space for catalysts has grown to include a wide variety of material and geometric parameters, accurate PDE-based modeling and simulation of such systems has become necessary to understand and optimize the coupled reaction and transport processes that determine cell performance. High fidelity models, however, pose immense computational challenges. Physical and chemical processes occur over a wide variety of spatiotemporal scales, ranging from pore-scale reaction and transport to device-scale gradients in species concentrations. Additionally, the spatial orientation of these features necessitates modeling in multiple dimensions, and transport occurs simultaneously through two phases located within randomly-intermixed porous regions. As a result of all these factors, numerical simulation of high fidelity models is prohibitively expensive.In this work, we develop, numerically simulate, and validate a predictive model that efficiently incorporates a wide range of physical and chemical processes occurring in catalyst microenvironments with intermixed hydrophobic and hydrophilic regions. We employ homogenization as a means of model reduction, constructing a medium fidelity model that captures both microenvironment geometric scales and full device scales in 3D. Our model, coupled with the appropriate numerical methods for treatment of stiff and multi-dimensional problems, permits low-cost exploration of the high-dimensional parameter space associated with recently developed catalysts, offering quantitative insight into the optimal design of microenvironments for CO and CO2 electroreduction.This work is supported by the United States Department of Energy under grant DE-SC0021633.

Efficient radiation treatment planning based on voxel importance

Objective. Radiation treatment planning (RTP) involves optimization over a large number of voxels, many of which carry limited information about the clinical problem. We propose an approach to reduce the large optimization problem by only using a representative subset of informative voxels. This way, we drastically improve planning efficiency while maintaining the plan quality. Approach. Within an initial probing step, we pre-solve an easier optimization problem involving a simplified objective from which we derive an importance score per voxel. This importance score is then turned into a sampling distribution, which allows us to subsample a small set of informative voxels using importance sampling. By solving a—now reduced—version of the original optimization problem using this subset, we effectively reduce the problem’s size and computational demands while accounting for regions where satisfactory dose deliveries are challenging. Main results. In contrast to other stochastic (sub-)sampling methods, our technique only requires a single probing and sampling step to define a reduced optimization problem. This problem can be efficiently solved using established solvers without the need of modifying or adapting them. Empirical experiments on open benchmark data highlight substantially reduced optimization times, up to 50 times faster than the original ones, for intensity-modulated radiation therapy, all while upholding plan quality comparable to traditional methods. Significance. Our novel approach has the potential to significantly accelerate RTP by addressing its inherent computational challenges. We reduce the treatment planning time by reducing the size of the optimization problem rather than modifying and improving the optimization method. Our efforts are thus complementary to many previous developments.

Microstructure Modeling of Transport and Kinetics in Solid State Batteries with LLZO Solid Electrolyte Fabricated Using the Freeze-Tape-Casting Method

In traditional Li-ion batteries with liquid electrolyte, the transport of lithium ions in the electrolyte and lithium in the active material within a porous electrode are studied extensively. The continuum-scale modeling of these transport phenomena considers a homogenized simulation domain consisting of liquid electrolyte and solid active material with effective transport properties accounting for the heterogeneity [1]. There have been fewer studies that consider digitized versions of the experimentally imaged microstructure in continuum-scale modeling studies, particularly to study mechanics [2]. Despite of the computational challenges, there are inherent advantages in using fully resolved microstructure modeling to improve fundamental understanding of the relevant physical and chemical phenomena in porous electrodes, such as localized degradation and stress concentration. In the case of solid electrolytes, specifically scaffold-type LLZO solid electrolytes, microstructure modeling is of even more relevance, particularly to study anisotropic transport, localized degradation phenomena, stress and deformation leading to loss of contact, and chemo-mechanics. Thus far, microstructure modeling studies on experimentally obtained microstructure consisting of solid electrolyte and active material have been quite limited [3]. The studies that have explored this realm have employed voxel-based mesh to discretize the computation domain [3]. While these methods are sufficient proof of concept, the lack of precision in defining the interface separating active material and electrolyte when using voxel-based mesh provides a source of potentially significant numerical error. Without a highly resolved interface mesh, the total interfacial area increases artificially which leads to inaccuracies in calculations of local reaction rate and overpotential. A highly resolved, smooth interface is also necessary when simulating mechanical deformation and stress field in the microstructure. In the present investigation, we model mass transport and charge transport in the active material/electrolyte microstructure to simulate concentration and potential fields with varying mesh types in order to compare their effectiveness and characterize the error that can be attributed to the mesh type chosen, particularly in the case of voxel-based meshes. Additionally, we study the effect of microstructure domain length chosen in the two directions perpendicular to the shortest length (electrode thickness) as well as boundary condition formulation on the boundaries in these axes on the simulation results. The microstructures studied in this work are derived from the image stacks of the bi/trilayer LLZO solid electrolyte fabricated using freeze-tape-casting method [4]. Overall, the present work aims to develop a better understanding of the best practices in microstructure modeling, particularly when using microstructure image stacks obtained from fabricated samples.

Substitution of a Microstructure-Simulation with a Data-Driven Approach for Modelling Mechanical Degradation of Electrodes

One significant aspect of battery aging involves the mechanical degradation of battery electrodes. The intercalation of ions into the active material particles of a lithium-ion battery creates a concentration profile along the radial direction of the active material particle, influenced by transport properties and kinetics. Mechanical stresses arise due to the spatial constraints of intercalated ions, leading to the mechanical degradation of the electrode.Common cathode materials used in practical applications, such as Lithium Nickel Manganese Cobalt Oxide (NMC) or Lithium Iron Phosphate (LFP), undergo a transition from a lithium-poor phase to a lithium-rich phase during the intercalation process. Along this phase boundary, high mechanical stresses occur due to the inconsistency of the concentration profile, resulting in particle fractures. This can lead to electrical isolation and, consequently, the electrochemical inertization of the active particle material, manifested as a reduced battery capacity on a macroscopic scale.Simulating these processes poses computational challenges. Given current computational power, accurately simulating a sufficient number of cycles for a microstructure simulation, which describes transport processes within a battery, is computationally prohibitive. Therefore, this study employs data-driven models. To predict radial concentration profiles within the particle, a physics-informed neural network (PINN) proves useful by extending the cost function with the transport equation. Classical neural networks (ANN) are utilized to calculate the electrical voltage of active material particles. These models are trained with results from microstructure simulations to develop a grey-box model, enabling the inclusion of mechanical degradation in the electrode simulation.Mechanical stresses and degradations are thus investigated under various galvanostatic discharge rates to define an aging-resistant operational window for the battery.

Compressed point cloud classification with point-based edge sampling

3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method “CPCC-PES” that focuses on point cloud’s edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research.

KeldyshQFT: A C++ codebase for real-frequency multiloop functional renormalization group and parquet computations of the single-impurity Anderson model.

We provide a detailed exposition of our computational framework designed for the accurate calculation of real-frequency dynamical correlation functions of the single-impurity Anderson model in the regime of weak to intermediate coupling. Using quantum field theory within the Keldysh formalism to directly access the self-energy and dynamical susceptibilities in real frequencies, as detailed in our recent publication [Ge et al., Phys. Rev. B 109, 115128 (2024)], the primary computational challenge is the full three-dimensional real-frequency dependence of the four-point vertex. Our codebase provides a fully MPI+OpenMP parallelized implementation of the functional renormalization group (fRG) and the self-consistent parquet equations within the parquet approximation. It leverages vectorization to handle the additional complexity imposed by the Keldysh formalism, using optimized data structures and highly performant integration routines. Going beyond the results shown in the previous publication, the code includes functionality to perform fRG calculations in the multiloop framework, up to arbitrary loop order, including self-consistent self-energy iterations. Moreover, implementations of various regulators, such as hybridization, interaction, frequency, and temperature, are supplied.

Leveraging neural networks to estimate parameters with confidence intervals

AbstractThis manuscript presents a proof of concept for the estimation of parameters in a bioprocess while providing reliable confidence intervals. Specifically, Bayesian inference is used to estimate the uncertainty in the prediction of a parameter due to the presence of measurement noise in the process. The resultant joint probability distribution is utilized to infer the confidence interval of the resultant estimates. This method is numerically applied using a technique known as nested sampling. This algorithm iteratively samples parameters from a pre‐determined range of values to compare model predictions and obtain a probability density function. One challenge typically associated with this algorithm is in the determination of the prediction error, especially when a high‐fidelity dynamic model is being utilized. For the motivating example in the present manuscript, where a high‐fidelity simulated bioprocess is being considered, the use of the high‐fidelity model provided by Sartorius AG as part of the estimation algorithm poses computational challenges. To overcome this challenge, a universal approximator such as a parameterized neural network is used. This neural network is designed to simulate the results of the first principles model (while also capturing the dependence of the model parameters on the output), and once trained can provide near instantaneous results making the use of nested sampling computationally tractable for the application. Simulation results demonstrate the feasibility and capability of the proposed approach.

Compressing the two-particle Green’s function using wavelets: Theory and application to the Hubbard atom

Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly, exciting breakthroughs are enabling the computation of non-zero temperature correlation functions. However, computational challenges arise due to constraints in resources and memory limitations, especially in scenarios involving complex Green’s functions and lattice effects. Leveraging the principles of signal processing and data compression, this paper explores the wavelet decomposition as a versatile and efficient method for obtaining compact and resource-efficient representations of the many-body theory of interacting systems. The effectiveness of the wavelet decomposition is illustrated through its application to the representation of generalized susceptibilities and self-energies in a prototypical interacting fermionic system, namely the Hubbard model at half-filling in its atomic limit. These results are the first proof-of-principle application of the wavelet compression within the realm of many-body physics and demonstrate the potential of this wavelet-based compression scheme for understanding the physics of correlated electron systems.

Quantum dynamical Hamiltonian Monte Carlo

One of the open challenges in quantum computing is to find meaningful and practical methods to leverage quantum computation to accelerate classical machine-learning workflows. A ubiquitous problem in machine-learning workflows is sampling from probability distributions that we only have access to via their log probability. To this end, we extend the well-known Hamiltonian Monte Carlo (HMC) method for Markov chain Monte Carlo (MCMC) sampling to leverage quantum computation in a hybrid manner as a proposal function. Our new algorithm, Quantum Dynamical Hamiltonian Monte Carlo (QD-HMC), replaces the classical symplectic integration proposal step with simulations of quantum-coherent continuous-space dynamics on digital or analog quantum computers. We show that QD-HMC maintains key characteristics of HMC, such as maintaining the detailed balanced condition with momentum inversion, while also having the potential for polynomial speedups over its classical counterpart in certain scenarios. As sampling is a core subroutine in many forms of probabilistic inference, and MCMC in continuously parametrized spaces covers a large class of potential applications, this work widens the areas of applicability of quantum devices. Published by the American Physical Society 2024

1D -FNO - Residual convolutional neural network autoregressive prediction model for deep water wave train evolution based on high-order spectral (HOS) data

Accurate prediction of deep-water wave dynamics is essential for ocean engineering, meteorology, and maritime safety. Traditional physical models, though effective, face significant computational and time challenges with complex nonlinear dynamics. This study introduces an autoregressive prediction model using a one-dimensional adaptive Fourier Neural Operator-Residual Convolutional Neural Network (1D-FNO-ResNet) to enhance prediction accuracy. We developed a high-precision single-step prediction model capable of capturing wave characteristics such as height and fluid particle velocity. A fine-tuning training strategy based on multi-step prediction errors was proposed for autoregressive evolution. Performance comparisons between the ResNet and FNO-ResNet models were made under various fine-tuning steps. A correction model was also introduced to control error accumulation, improving autoregressive prediction steps. The model's generalization performance was validated with modulated and random wave trains under different conditions, optimizing predictive stability and accuracy. This approach demonstrates efficient 1D deep-water wave evolution prediction, offering a novel deep learning solution for wave dynamics prediction in ocean engineering, supporting wave energy conversion device design and optimization.

Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte

A growing demand to visualize polymer models in liquid poses a computational challenge in molecular dynamics (MD) simulation, as this requires emerging models under suitable force fields (FFs) to capture the underlying molecular behaviour accurately. In our present study, we have employed TIP3P potential on water and all atomistic optimized potentials for liquid simulations FFs to study the liquid electrolyte behavior of phosphazene-based polymer by considering its potential use in lithium-ion polymer batteries. We have explored the polymer's local structure, chain packing, wettability, and hydrophobic tendencies against the silicon surface using a combination of a pseudocontinuum model in MD simulation, and surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. The finding yields invaluable insights into the molecular architecture of phosphazene. This approach identifies the importance of hydrophobic interactions with air and hydrophilic units with water molecules in understanding the behavior and properties of phosphazene-based polymers at interfaces, contributing to its advancements in materials science. The MD study uniquely captures traces of the polymer-ion linkage, which is observed to become more pronounced with the increase in polymer weight fraction. The theoretical observation of this linkage's influence on lithium-ion diffusion motion offers valuable insights into the fundamental physics governing the behavior of atoms and molecules within phosphazene-based polymer electrolytes in aqueous environments. Further these predictions are corroborated in the molecular-level depiction at the air-aqueous interface, as evidenced from the OH-oscillator strength variation measured by the SFG spectroscopy.The fundamental findings from this study open new avenues for utilizing MD simulation as a versatile methodology to gain profound insights into intermolecular interactions of polymer. It could be useful in the application of biomedical and energy-related research, such as polymer lithium-ion batteries, fuel cells, and organic solar cells.

CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic

The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).

Robust optimization approaches in inventory management: Part B - the comparative study

This comparative study, constituting Part B of our extensive investigation into Robust Optimization (RO) in inventory management, builds on the foundational insights from Part A’s survey. It conducts a thorough analysis of various RO formulations and algorithms, emphasizing their practical application, efficacy, and computational considerations in inventory management contexts. This study is meticulously structured to address the pivotal inquiries identified in Part A, encompassing the effective representation of uncertainty, the selection of optimal decision criteria, the influence of decision rules on inventory management performance, computational challenges, and the adaptability of these methods to evolving technological and market conditions. This research juxtaposes theoretical findings with empirical data, offering a comprehensive evaluation of the strengths, limitations, and practical implications of each robust inventory model discussed in the literature. This investigation not only complements the survey in Part A but also serves as a standalone, in-depth contribution to robust inventory management.

A cross-modal high-resolution image generation approach based on cloud-terminal collaboration for low-altitude intelligent network

The advancement of digitization and automation in Low Altitude Intelligent Networking (LAIN) is constrained by limited computational resources and the absence of a dedicated modal transformation mechanism, affecting the performance of latency-sensitive missions. This study addresses these challenges by proposing a Downscaling Reconstruction Multi-scale Locally Focused Generative Adversarial Network (DR-MFGAN) with Federated Learning (FL). This integration employs wavelet transform downscaling and zero-shot residual learning techniques to create noise-suppressed image pairs, ultimately facilitating high-quality image reconstruction. The core network structure is composed of multidimensional residual blocks and generative confrontation network, and feature extraction is further enhanced through cross channel attention mechanism. Finally, distributed training based on Federated Learning ensures the training effectiveness of nodes with small data volumes.Experimental results demonstrate significant improvements: an 18.18% reduction in Mean Squared Error (MSE), a 33.52% increase in Peak Signal to Noise Ratio (PSNR), and a 39.54% improvement in Learned Perceptual Image Patch Similarity (LPIPS). The edge terminal can provide high-resolution imagery with limited data, achieving precise cross-modal transformations. This approach enhances LAIN capabilities, addressing computational and transformation challenges to support critical latency-sensitive missions.

Model predictive controls for residential buildings with heat pumps: Experimentally validated archetypes to simplify the large-scale application

Exploiting the energy flexibility resulting from thermal loads management in buildings is one of the most promising solutions to contribute to the energy transition targets. To offer energy flexibility to the grid, the building should meet the requirements: (i) it needs electrically powered generation (e.g., through Heat Pumps) and (ii) advanced control techniques (e.g., Model Predictive Controls) must be implemented. In recent years, the scientific community has produced many studies demonstrating the potential of Model Predictive Controls combined with Heat Pumps to exploit energy flexibility in buildings. However, a large-scale deployment of such control techniques is still far off, both because of the not yet widespread use of Heat Pumps and the computational challenges involved in implementing them. The aim of this study is to contribute to the deployment of advanced control techniques for Heat Pumps systems in buildings by simplifying their implementation. At this aim, validated archetypes of Model Predictive Controls for Heat Pumps in residential buildings are proposed. The availability of archetypes can greatly facilitate the practical application of Model Predictive Control. In fact, they are adaptable to the characteristics of different buildings and Heat Pump installations and their structure is designed to have an acceptable trade-off between calculation time and prediction reliability. The archetypes cover three different types of heating systems: low temperature radiators without (i) and with (ii) integrated heat storage devices and (iii) underfloor heating system. All archetypes are applied to a real Heat Pump and validated through experimental campaigns. During the experimental test all the archetypes proved to be effective in controlling the real system and the results showed good reliability of the prediction model in the control (for all archetypes a Root Mean Square Error lower than 0.44 °C was obtained) and an optimizer success rate in Model Predictive Controls greater than 91 %. The archetypes proposed are provided as open-source tools that can be reused for similar cases to facilitate Model Predictive Controls implementation in heat pump systems.

MKELM based multi-classification model for foreign accent identification

The automatic identification of foreign accents can play a crucial role in various speech systems, including speaker identification, e-learning, telephone banking, and more. Additionally, it can greatly enhance the robustness of Automatic Speech Recognition (ASR) systems. Non-native accents in speech signals are characterized by distinct pronunciations, prosody, and voice characteristics of the speaker. However, automatically identifying foreign accents poses significant challenges, particularly in the context of multi-class modeling. Multi-classification models face difficulties in achieving high performance and dealing with computational challenges when confronted with multi-dimensional and unbalanced datasets, such as those with more than two accents. Furthermore, the choice of features remains a bottleneck problem for Foreign Accent Identification (FAID), further hindering performance in these tasks. Consequently, the accuracy of current systems is typically low. To address these challenges, this paper proposes a framework based on the Multi-Kernel Extreme Learning Machine (MKELM) model for the multi-classification of FAID. The MKELM model utilizes a novel weighted scheme to classify various non-native English accents, including Arabic, Chinese, Korean, French, and Spanish. The model first combines Mel-frequency cepstral coefficients (MFCCs) and prosodic features as input, trains pairwise binary classifiers independently, and subsequently employs a weighting scheme to distinguish between classes and identify accents. Through experiments, the proposed model achieves an accuracy rate of 84.72% using a paired weighting scheme. In contrast, the accuracy rate drops to 66.5% when employing the traditional non-weighted multi-classification scheme. A comparison with other models demonstrates the significant advantages of the proposed model in FAID multi-class classification, showcasing improved accuracy, reduced computational complexity (requiring fewer computations, faster learning rates, and shorter training time), and enhanced stability compared to state-of-the-art classification methods.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein–Protein Interaction

The study of protein–protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. The huge data growth of PPI leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being interpretable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments conducted across various scales of PPI datasets have conclusively demonstrated that the laruGL-PPI method surpasses the most advanced PPI prediction techniques currently available, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.

Optimized multi-point hemispherical grid model with adaptive grid division based on the prior information of multipath error

The multi-point hemispherical grid model (MHGM) utilizes residual of double-differenced observations to extract precise multipath error information. It models the entire network of multipath error effects across different stations to achieve effective error correction. However, because all the parameters are estimated collectively using the least squares method, the increased number of grid point parameters can significantly consume memory, CPU, and other computing resources required for modeling. In response to the computational resource consumption challenge associated with fixed-resolution MHGM in multi-station applications, a space domain adaptive grid division method is proposed to optimize the modeling of multipath errors. This approach utilizes prior distribution information of multipath errors to optimize the grid structure. It reduces the number of grids in areas where multipath errors exhibit minimal changes, and provides detailed parameterization for areas with significant variations. Experimental results demonstrate the effectiveness of this method in significantly reducing the number of estimated parameters using MHGM. In statistical analysis of double-differenced phase observation residuals with fixed ambiguities, as the number of estimated parameters in the MHGM decreases to only 24.6 % of the fixed-resolution approach, memory usage during parameter estimation remains a mere 6 % of that required in the fixed-resolution approach. This highlights its potential value in mitigating multipath errors when modeling GNSS large-scale network data.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations.

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Compressing the memory variables in constant-Q viscoelastic wave propagation via an improved sum-of-exponentials approximation

Earth introduces strong attenuation and dispersion to propagating waves. The time-fractional wave equation with very small fractional exponent, based on Kjartansson's constant-Q theory, is widely recognized in the field of geophysics as a reliable model for frequency-independent Q anelastic behavior. Nonetheless, the numerical resolution of this equation poses considerable challenges due to the requirement of storing a complete time history of wavefields. To address this computational challenge, we present a novel approach: a nearly optimal sum-of-exponentials (SOE) approximation to the Caputo fractional derivative with very small fractional exponent, utilizing the machinery of generalized Gaussian quadrature. This method minimizes the number of memory variables needed to approximate the power attenuation law within a specified error tolerance. We establish a mathematical equivalence between this SOE approximation and the continuous fractional stress-strain relationship, relating it to the generalized Maxwell body model. Furthermore, we prove an improved SOE approximation error bound to thoroughly assess the ability of rheological models to replicate the power attenuation law. Numerical simulations on constant-Q viscoacoustic equation in 3D homogeneous media and variable-order P- and S- viscoelastic wave equations in 3D inhomogeneous media are performed. These simulations demonstrate that our proposed technique accurately captures changes in amplitude and phase resulting from material anelasticity. This advancement provides a significant step towards the practical usage of the time-fractional wave equation in seismic inversion.