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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Design Rules for Two–Dimensional Organic Semiconductor–Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures

Two-dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial-and-error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden-Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten-thousand RP-phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near-perfect predictions of positive and negative literature examples, predict trade-offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D-compatible ligand design-space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Although F-Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter- and intra-Chain interactions responsible for self-healing of F-Containing copolymers composed of pentafluorostyrene and n-butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ-σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic-σ-lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self-healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic-σ-lock (~70 %) contributions. These interactions are significantly diminished for non-self-healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non-Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self-healable batteries and energy storage devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Self‐Healable Poly(ionic liquid) Copolymers Driven by Polar and Dipolar Forces

Abstract Commodity aliphatic and aromatic acrylic‐based copolymers self‐heal due to ubiquitouskey‐and‐lock,ring‐and‐lock, andfluorophilic‐σ‐lockvan der Waals (vdW) interactions. However, the role of these interactions in the presence of covalently copolymerized ionic liquid (IL) is not known. This study is driven by the hypothesis that covalently incorporated cation–anion pairs to form poly(ionic liquid) copolymers (PILCs) can perturb inter‐ or intra‐chain vdW interactions reflected in mechanical and electrical responses. To test this hypothesis, we synthesized a series of PILCs comprising of pentafluorostyrene (PFS) and imidazolium‐based IL monomers with variable‐length aliphatic tails (methyl and butyl). Using a combination of 2D 1 H‐ 1 H and 19 F ‐ 19 F NOESY NMR and FTIR measurements supplemented by molecular dynamic (MD) simulations, these studies demonstrate that preferentially alternating/random PILCs topologies facilitate self‐healing. The introduction of cation–anion moieties modifies thefluorophilic‐σ‐lockinteractions and, along with longer aliphatic tails ─(CH 2 ) 3 CH 3 covalently attached to the imidazolium cation, enhances cation‐anion mobility, thus faster recovery from mechanical damage occurs. These findings underline how precise control over dipolar and ionic interactions through copolymer composition enables self‐healing in PILCs. These insights may open pathways for designing sustainable, mechanically resilient materials for applications in energy storage and energy harvesting.

Chemistry↗
DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Kinetics and Mechanisms of ZnO to ZIF-8 Transformations in Supercritical CO2 Revealed by in situ X-ray Diffraction

ZIF-8 was synthesized in supercritical carbon dioxide (scCO2). In situ powder X-ray diffraction, ex situ microscopy and simulations, provides a encompassing view of the formation of ZIF-8 and intermediary ZnO@ZIF-8 composites in this nontraditional solvent. Time-resolved imaging exposed divergent physicochemical reaction pathways from previous studies of the growth of anisotropic ZIF-8 core@shell structures in traditional solvents. Synthetically relevant physio-chemical properties of scCO2 were integrated into classical nucleation theory, relating interfacial forces with 3D nu-cleation outcomes. The kinetics of crystallization were examined and displayed a characteristic signature of time- and temperature-dependent mechanisms over the extent of the reaction. Lastly, we show that subtle factors, such as the extent of reaction and the size/shape of sacrificial templates can tailor ZIF-8 composition and size, eliciting control over hierarchical porosity in a nonconventional green solvent.

Sinnwell, Michael A.↗
DOE OSTI2024

Underlying Roles of Polyol Additives in Promoting CO2 Capture in PEI/Silica Adsorbents

Solid-supported amines having low molecular weight branched poly(ethylenimine) (PEI) physically impregnated into porous solid supports are promising adsorbents for CO2 capture. Co-impregnating short-chain poly(ethylene glycol) (PEG) together with PEI alters the performance of the adsorbent, delivering improved amine efficiency (AE, mol CO2 sorbed / mol N) and faster CO2 uptake rates. To uncover the physical basis for this improved gas capture performance, we probed the distribution and mobility of the polymers in the pores via small angle neutron scattering (SANS), solid-state NMR, and molecular dynamic (MD) simulation studies. SANS and MD simulations reveal that PEG displaces wall-bound PEI, making amines more accessible for CO2 sorption. Solid-state NMR and MD simulation suggest intercalation of PEG into PEI domains, separating PEI domains and reducing amine-amine interactions, providing potential PEG-rich and amine-poor interfacial domains that bind CO2 weakly via physisorption while providing facile pathways for CO2 diffusion. Contrary to a prior literature hypothesis, no evidence is obtained for PEG facilitating PEI mobility in solid supports. Instead, the data suggest that PEG chains coordinate to PEI, form larger bodies with reduced mobility compared to PEI alone. We also demonstrate promising CO2 uptake and desorption kinetics at varied temperatures, given by favorable amine distribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Direct Synthesis of Layer-Tunable and Transfer-Free Graphene on Device-Compatible Substrates Using Ion Implantation Toward Versatile Applications

Direct synthesis of layer-tunable and transfer-free graphene on technologically important substrates is highly valued for various electronics and device applications. State of the art in the field is currently a two-step process: a high-quality graphene layer synthesis on metal substrate through chemical vapor deposition (CVD) followed by delicate layer transfer onto device-relevant substrates. Here, we report a novel synthesis approach combining ion implantation for a precise graphene layer control and dual-metal smart Janus substrate for a diffusion-limiting graphene formation to directly synthesize large area, high quality, and layer-tunable graphene films on arbitrary substrates without the post-synthesis layer transfer process. Carbon (C) ion implantation was performed on Cu–Ni film deposited on a variety of device-relevant substrates. A well-controlled number of layers of graphene, primarily monolayer and bilayer, is precisely controlled by the equivalent fluence of the implanted C-atoms (1 monolayer ~4 × 10 15 C-atoms/cm 2 ). Upon thermal annealing to promote Cu-Ni alloying, the pre-implanted C-atoms in the Ni layer are pushed toward the Ni/substrate interface by the top Cu layer due to the poor C-solubility in Cu. As a result, the expelled C-atoms precipitate into a graphene structure at the interface facilitated by the Cu-like alloy catalysis. After removing the alloyed Cu-like surface layer, the layer-tunable graphene on the desired substrate is directly realized. The layer-selectivity, high quality, and uniformity of the graphene films are not only confirmed with detailed characterizations using a suite of surface analysis techniques but more importantly are successfully demonstrated by the excellent properties and performance of several devices directly fabricated from these graphene films. Molecular dynamics (MD) simulations using the reactive force field (ReaxFF) were performed to elucidate the graphene formation mechanisms in this novel synthesis approach. With the wide use of ion implantation technology in the microelectronics industry, this novel graphene synthesis approach with precise layer-tunability and transfer-free processing has the promise to advance efficient graphene-device manufacturing and expedite their versatile applications in many fields.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural Changes to the Gd‐DTPA Complex at Varying Ligand Protonation State

Abstract Diethylenetriaminepentaacetic acid (DTPA) is a chelating agent whose complex with the Gd 3+ ion is used in medical imaging. DTPA is also used in lanthanide‐actinide separation processes. As protonation of the DTPA ligand can facilitate dissociation of the Gd 3+ ion from the Gd‐DTPA complex, this work investigates the coordination structures of the aqueous Gd 3+ ion and its environment when chelated by DTPA in eight different DTPA protonation states. Both classical and ab initio molecular dynamics (MD) simulations are conducted to model the solvated complexes. Extended X‐ray absorption fine structure (EXAFS) measurements of the Gd 3+ aqua ion, and the Gd‐DTPA complex at pH 1 and 11, are compared to EXAFS spectra predicted from the MD simulations to verify the accuracy of the MD structures. The findings of this work provide atomic‐level details into the fluctuating Gd‐DTPA complex environment as the DTPA ligand gradually detaches from the Gd 3+ ion with increased protonation.

Chemistry↗
DOE OSTI2021

CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 μs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. Additionally, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. Finally, the capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Methyl α‐D‐galactopyranosyl‐(1→3)‐β‐D‐galactopyranoside and methyl β‐D‐galactopyranosyl‐(1→3)‐β‐D‐galactopyranoside: Glycosidic linkage conformation determined from MA'AT analysis

Abstract MA'ATanalysis has been applied to two biologically‐importantO‐glycosidic linkages in two disaccharides, α‐D‐Galp‐(1→3)‐β‐D‐GalpOMe (3) and β‐D‐Galp‐(1→3)‐β‐D‐GalpOMe (4). Using density functional theory (DFT) to obtain parameterized equations relating a group of trans‐O‐glycosidic NMR spin‐couplings to eitherphi(ϕ') orpsi(ψ'), and experimental 3 J COCH , 2 J COC , and 3 J COCC spin‐couplings measured in aqueous solution in 13 C‐labeled isotopomers, probability distributions ofϕ'andψ'in each linkage were determined and compared to those determined by aqueous 1‐μs molecular dynamics (MD) simulation. Good agreement was found between theMA'ATand single‐state MD conformational models of these linkages for the most part, with modest (approximately <15°) differences in the mean values ofϕ'andψ', although the envelope of allowed angles (encoded in circular standard deviations or CSDs) is consistently larger forϕ'determined fromMA'ATanalysis than from MD for both linkages. TheMA'ATmodel of the α‐Galp‐(1→3)‐β‐Galplinkage agrees well with those determined previously using conventional NMR methods ( 3 J COCH values and/or 1 H‐ 1 H NOEs), but some discrepancy was observed for the β‐Galp‐(1→3)‐β‐Galplinkage, which may arise from errors in the conventions used to describe the linkage torsion angles. Statistical analyses of X‐ray crystal structures show ranges ofϕ'andψ'for both linkages that include the mean angles determined fromMA'ATanalyses, although both angles adopt a wide range of values in the crystalline state, withϕ'in β‐Galp‐(1→3)‐β‐Galplinkages showing greater‐than‐expected conformational variability.

Chemistry↗
DOE OSTI2020

Molecular dynamics analysis of the binding of human interleukin-6 with interleukin-6 α-receptor

Human interleukin-6 (hIL-6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL-6 binding to the IL-6 receptor (IL-6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti-IL-6 and IL-6R antibodies are clinically approved inhibitors of IL-6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL-6 antagonists derived from understanding the structural basis for IL-6/IL-6Rα interactions. In this work, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL-6 with IL-6Rα. A comparison with MD of apo hIL-6 reveals that the binding of hIL-6 to IL-6Rα induces structural and dynamic rearrangements in the AB loop region of hIL-6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL-6)- Glu182 (IL-6Rα) and Arg184 (hIL-6)- Glu183 (IL-6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL-6/IL-6Rα complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Co-Assembly of Carbon Nanotube Porins into Biomimetic Peptoid Membranes

Robust and cost-effective membrane-based separations are essential to solving many global crises, such as the lack of clean water. Even though the current polymer-based membranes are widely used for separations, their performance and precision can be enhanced by using a biomimetic membrane architecture that consists of highly permeable and selective channels embedded in a universal membrane matrix. Researchers have shown that artificial water and ion channels, such as carbon nanotube porins (CNTPs), embedded in lipid membranes can deliver strong separation performance. However, their applications are limited by the relative fragility and low stability of the lipid matrix. In this work, we demonstrate that CNTPs can co-assemble into two-dimension (2D) peptoid membrane nanosheets, opening up a way to produce highly programmable synthetic membranes with superior crystallinity and robustness. A combination of molecular dynamics (MD) simulations, Raman spectroscopy, X-ray diffraction (XRD), and atomic force microscopy (AFM) measurements to verify the co-assembly of CNTP and peptoids are used and show that it does not disrupt peptoid monomer packing within the membrane. In conclusion, these results provide a new option for designing affordable artificial membranes and highly robust nanoporous solids.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of Methyl Groups on Formation of Ordered or Layered Graphitic Materials from Aromatic Molecules

Abstract Developing functionally complex carbon materials from small aromatic molecules requires an understanding of how the chemistry and structure of its constituent molecules evolve and crosslink, to achieve a tailorable set of functional properties. Here, molecular dynamics (MD) simulations are used to isolate the effect of methyl groups on condensation reactions during the oxidative process and evaluate the impact on elastic modulus by considering three monodisperse pyrene‐based systems with increasing methyl group fraction. A parameter to quantify the reaction progression is designed by computing the number of new covalent bonds formed. Utilizing the previously developed MD framework, it is found that increasing methylation leads to an almost doubling of bond formation, a larger fraction of the new bonds oriented in the direction of tensile stress, and a higher basal plane alignment of the precursor molecules along the direction of tensile stress, resulting in enhanced tensile modulus. Additionally, via experiments, it is demonstrated that precursors with a higher fraction of methyl groups result in a higher alignment of molecules. Moreover, increased methylation results in the lower spread of single molecule alignment which may lead to smaller variations in tensile modulus and more consistent properties in carbon materials derived from methyl‐rich precursors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ionic Liquid‐Driven Modulation of DNA Brush Morphology on Nanoparticle Surfaces

Here, the morphology of DNA is strongly influenced by its surrounding environment, including factors such as pH, salt type and valency, and the presence of polymers. Inorganic salts are known to reduce the DNA chain length through mechanisms like electrostatic screening and ion bridging. In contrast, ionic liquids, a new class of organic salts, have previously been found to increase the DNA chain length, indicating a distinct mode of interaction between the ionic liquid and DNA chains. This study utilizes self-assembled DNA-AuNPs as a model system to examine changes in the DNA chain morphology and the nanoscale interaction mechanisms in an ionic liquid environment. The DNA chain lengths are measured in solution using X-ray scattering measurements at varying concentrations of two imidazolium ([$BMIM$] acetate and [$EMIM$] acetate) based ionic liquids. Additionally, Molecular Dynamics (MD) simulations are performed mimicking the experimental system. Our results suggest an interplay of electrostatic and groove-binding interactions governing the DNA chain morphology, which depends on IL concentration and the composition of the DNA chains. It has been found that for DNA chains with majority ssDNA, electrostatic interaction dominate, however with increasing composition of double strands, the DNA chains exhibit compaction due to a non-electrostatic hydrophobic groove-binding mechanism.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Investigations of water/oxide interfaces by molecular dynamics simulations

Water/oxide interfaces are ubiquitous on earth and show significant influence on many chemical processes. For example, understanding water and solute adsorption as well as catalytic water splitting can help build better fuel cells and solar cells to overcome our looming energy crisis; the interaction between biomolecules and water/oxide interfaces is one hypothesis to explain the origin of life. However, knowledge in this area is still limited due to the difficulty of studying water/solid interfaces. As a result, research using increasingly sophisticated experimental techniques and computational simulations has been carried out in recent years. Although it is difficult for experimental techniques to provide detailed microscopic structural information, molecular dynamics (MD) simulations have satisfactory performance. In this report, we discuss classical and ab initio MD simulations of water/oxide interfaces. Generally, we are interested in the following questions: How do solid surfaces perturb interfacial water structure? How do interfacial water molecules and adsorbed solutes affect solid surfaces and how do interfacial environments affect solvent and solute behavior? Finally, we discuss progress in the application of neural network potential based MD simulations, which offer a promising future because this approach has already enabled ab initio level accuracy for very large systems and long trajectories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structure/property relationship of semicrystalline polymers during tensile deformation: a molecular dynamics approach

Molecular dynamics (MD) simulation with an ab initio numerical model is conducted to study the structure–property relationship of semicrystalline polymers during uniaxial deformation. The effects of chain length, temperature, and strain rate on mechanical properties are discussed. The influences of microstructural evolution such as bond length, entanglement density, and chain orientation are also studied quantitatively. The temperature will greatly affect the chain conformation in amorphous domains, and the results revealed that the interaction of amorphous and crystalline domains played a crucial role during stretching. Accordingly, the yielding of semicrystalline polymers follows different mechanisms at temperatures above and below T g . A melt-recrystallization scheme is observed during yielding at higher temperatures, while destruction of crystal structures is observed at lower temperatures at the yield point. The correlated effects of different temperatures and strain rates on mechanical properties are examined. This research is part of our efforts to develop a digital twin of a real experiment for efficient optimization of polymer material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

97 MATHEMATICS AND COMPUTING↗