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Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices
Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x–y-Gly sequence is the defining motif of CMPs and is critical to their triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues—N-substituted glycine derivatives—present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone flexibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising peptoid substitutions predicted to stabilize the desired quaternary structure through side chain interactions and produce stable peptoid-based collagen-like triple helices. To experimentally test the computational predictions, a top candidate identified by the screen was synthesized and imaged using scanning electron microscopy to resolve fibril-like bundles consistent with collagen-like triple helices. This work predicts a number of CMP peptoid substitutions capable of forming stable triple-helical structures, presents a generalizable design strategy for engineering desired peptoid structures, and opens new avenues for the design of peptoid-based biomimetic materials.
Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures
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Topology optimization of composite structures with data-driven resin filling time manufacturing constraint
Not provided.
Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing
Abstract not provided
Data-Driven, In Situ, Relative Sensor Calibration Based on Waveform Fitting Moment Tensor Inversion
This article presents a deconvolution-based methodology for the relative calibration of acoustic emission sensors. The method uses the observations from multiple acoustic emission arrivals to estimate correction functions that account for instrument response and site effects. The corrections act on the measurements reshaping the wave arrivals, in many cases allowing visible discrimination between compressional and shear phases. The methodology is illustrated with the application on a dataset obtained during the hydraulic fracturing at triaxial stress conditions of a Colton sandstone block. A workflow for the estimation of full moment tensor solutions, including the generation of quality control attributes complements the presentation. The moment tensor solutions display fracture geometries mostly aligned with the main hydraulic fracture’s plane. The style of activation can be associated with an extensive stress regime before breakdown and to a compressional stress regime after breakdown, when the pumps were reversed to withdraw fluid from the block. In conclusion, the source types commonly deviate from the pure double-couple type; however, the deviations are under 30° in the case of opening fractures, and under 20° in the case of closing fractures. Therefore, the acoustic emissions in this experiment are the result of predominantly shearing dislocations.
Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines
Not provided.
Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease
Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.
A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels
Not provided.
DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems
Not provided.
Data-Driven Insights into the Structural Essence of Plasticity in High-Entropy Alloys
The heterogeneous mechanical response of a crystalline alloy with multiple principal elements was investigated using molecular dynamics simulations. The local configuration of the alloy in its quiescent state was characterized by the variables derived from the gyration tensor and the atomic electronegativity. A multivariate analysis identified the geometric and chemical factors that influenced the atomic packing variations. Further, upon straining, the non-affine displacement exhibited spatial heterogeneity. A statistical correlation was established between the local yield events and the specific features of the local configuration. Our findings, validated by the performance metrics analysis, provided a structural criterion for the instability mechanisms in high-entropy alloys (HEAs) and enhanced the understanding of their plasticity.
Applicability of data-driven respiratory motion correction to CZT SPECT myocardial perfusion imaging in the clinical setting: The birth of an old wish
No abstract prepared.
Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel
Here, predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.
Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel
Abstract To support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.
Data-driven texture design for reducing elastic and plastic anisotropy in titanium alloys
Not provided.