Article
Nanoscience & Nanotechnology
Omar Hussein, Maher Alghalayini, Shen J. Dillon, Fadi Abdeljawad
Summary: Sintering is a thermal processing technique used to consolidate particle compacts, with a particular interest in nanocrystalline particles. Atomistic simulations were used to investigate the sintering behavior of a series of [001] tilt GBs in Ni, revealing variations in particle neck growth rates as a function of GB type and suggesting a strategy for fabricating sintered materials with controlled nanostructured features.
ACS APPLIED NANO MATERIALS
(2021)
Article
Engineering, Mechanical
Chuanlong Xu, Xiaobao Tian, Wentao Jiang, Qingyuan Wang, Haidong Fan
Summary: This study investigates the migration mechanisms of symmetric tilt grain boundaries (STGBs) in magnesium using molecular dynamic simulations. The results show that the migration mechanisms of grain boundaries are significantly influenced by their structure, with small angle STGBs migrating through twin nucleation and growth, large angle STGBs migrating through the glide of grain boundary dislocations, and medium angle STGBs transforming into twin boundaries through the emission of lattice dislocations/stacking faults.
INTERNATIONAL JOURNAL OF PLASTICITY
(2022)
Article
Chemistry, Physical
Spencer Dahl, Toshihiro Aoki, Amitava Banerjee, Blas Pedro Uberuaga, Ricardo H. R. Castro
Summary: Lithium-ion batteries are crucial for improving energy storage solutions, and understanding the stability of interfaces plays a key role in enhancing battery capacity and cyclability. Chemical modification of interfaces offers the opportunity to create metastable states in cathodes to inhibit degradation. Atomistic simulations are effective in evaluating dopant interfacial segregation trends and can be used as a predictive tool for cathode design. The study investigated the segregation potential and stabilization effect of dopants in LiCoO(2) through computational analysis of surfaces and grain boundaries.
CHEMISTRY OF MATERIALS
(2022)
Article
Mechanics
Fatemeh Molaei, Amin Hamed Mashhadzadeh, Christos Spitas, Mohammad Reza Saeb
Summary: This study compares the fracture behavior and mechanical properties of diamond and gold in different conditions. Diamond shows brittle fracture mode while gold exhibits plastic deformation. Temperature has less impact on the fracture strength of diamond and more significant impact on gold. The mechanical properties of bicrystalline gold are lower than ideal monocrystalline gold.
ENGINEERING FRACTURE MECHANICS
(2022)
Article
Nanoscience & Nanotechnology
Nima Haghdadi, Hansheng Chen, Zibin Chen, Sudarsanam S. Babu, Xiaozhou Liao, Simon P. Ringer, Sophie Primig
Summary: Fluctuations in energy distribution during additive manufacturing can result in thermal transients, particularly in alloys. In this study, the complexities in a duplex stainless steel during laser powder bed fusion were investigated. The formation of Ni-Mn-Si rich phase at grain boundaries and local fluctuation in Cr and Fe concentrations were observed, providing precursors for Cr2N formation. These phases were attributed to severe thermal gyrations and thermal stresses associated with laser powder bed fusion.
SCRIPTA MATERIALIA
(2022)
Article
Nanoscience & Nanotechnology
Majid Laleh, Anthony E. Hughes, Mike Y. Tan, Gregory S. Rohrer, Sophie Primig, Nima Haghdadi
Summary: The study revealed that an austenitic stainless steel produced by additive manufacturing has relatively fine grains and a high population of Sigma 3 boundaries. The microstructure is mostly dominated by highly incoherent Sigma 3 boundaries, with other types of boundaries present in the grain boundary network. These findings demonstrate the potential to engineer the grain boundary network of materials via additive manufacturing.
SCRIPTA MATERIALIA
(2021)
Article
Materials Science, Multidisciplinary
Y. Cui, H. B. Chew
Summary: This study utilizes artificial neural networks for machine learning to predict local atomistic stress distributions along grain boundaries based on limited training data from molecular dynamics simulations. The accuracy of the ML algorithm is found to depend on the type, sequence, and distortion of grain boundary structural units, with accounting for these characteristics in the training dataset enabling accurate predictions of local atomistic stress distributions across various grain boundary structures. This ML-based constitutive modeling opens up possibilities for interpreting the equivalent stress state of atomistic structures beyond molecular dynamics, including structures from high-resolution transmission electron microscopy imaging and Density Functional Theory modeling.
Article
Engineering, Industrial
Hanlei Zhang, Yuankang Wang, Rafael Rodriguez De Vecchis, Wei Xiong
Summary: Heat treatments have a significant impact on the evolution of carbides in additive manufacturing. This study investigates the evolution pathways of MC- and M23C6-type carbides in a Haynes (R) 282 superalloy prepared by wire arc additive manufacturing (WAAM) through heat treatments. MC-type carbides are consistently present in the alloy and remain unchanged during heat treatments. On the other hand, M23C6-type carbides precipitate during an aging treatment and extend along the grain boundaries.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
(2022)
Article
Materials Science, Multidisciplinary
I. Chesser, R. K. Koju, A. Vellore, Y. Mishin
Summary: Atomistic computer simulations are used to investigate the atomic structure, thermal stability, and diffusion processes at the Al-Si interphase boundaries in composite materials. It is found that some stable orientation relationships observed in epitaxy experiments also exist at these interfaces. An interface-induced recrystallization mechanism can transform non-equilibrium interfaces into more stable states. Diffusion of Al and Si atoms in stable Al-Si interfaces is slower compared to diffusion in Al grain boundaries but can be accelerated in the presence of interface disconnections. A qualitative explanation for the sluggish interphase boundary diffusion is proposed, involving correlated atomic rearrangements in the form of strings and rings of collectively moving atoms.
Article
Chemistry, Physical
Changyuan Li, Feida Chen, Guojia Ge, Jiwei Lin, Zhangjie Sun, Minyu Fan, Ping Huang, Xiaobin Tang
Summary: Laser-based additive manufacturing offers a new method for rapid manufacturing of radiation-resistant materials for nuclear engineering systems. The sub-grain boundary (SGB) structure plays an important role in enhancing the radiation resistance of additive manufacturing materials. Experimental and simulation results show that the SGB structure acts as an efficient site for interstitial atoms and has a significant effect on the reduction of interstitial atom formation energy. However, an increase in SGB volume ratio after irradiation reduces the dislocation density on the SGB, impairing the radiation resistance of the material.
APPLIED SURFACE SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Fanbo Meng, Mingchang Zhang, Jin Huang, Wen Feng Lu, Jun Min Xue, Hao Wang
Summary: The study introduces a novel multinozzle, multimaterial printing device that addresses challenges in flexible device manufacturing, enhancing efficiency and stability in capacitor fabrication. Additionally, the interlayer reaction of materials improves battery performance, allowing for >2000 charge/discharge cycles with a high efficiency and capacity.
ADVANCED FUNCTIONAL MATERIALS
(2021)
Article
Engineering, Mechanical
Yun Hu, Youquan Wang, Jiangjing Xi, Ao Chen, Kamran Nikbin
Summary: Grain, grain boundaries, voids, alloying elements and other anomalies have a significant impact on the stress distribution at the sub-grain meso level in additively manufactured alloys, affecting the strain controlled low cycle fatigue. This paper proposes an idealized grain/grain boundary model using Voronoi tessellation technique to quantify the effects of different features on the LCF performance.
ENGINEERING FAILURE ANALYSIS
(2023)
Article
Materials Science, Multidisciplinary
Sunil Kumar, Sukalpan Nandi, Sudip Kumar Pattanayek, M. Madan, B. Kaushik, Roshan Kumar, Kurapati Gopala Krishna
Summary: In this study, molecular dynamics simulation was used to investigate the directional solidification of Fe-Cr-Ni steel under various temperature gradients. It was found that different crystal morphologies such as single crystals, nano-grains, multiple stacking faults, and twins formed during directional solidification depending on the temperature gradient. The evolution of these crystalline structures was characterized using adaptive common neighbor analysis, surface area, volume, and dimensionless aspect ratio. This investigation contributes to the pedagogical understanding of the effects of thermal gradients on the evolution characteristics of crystal morphology in modern additive manufacturing processes.
MATERIALS CHEMISTRY AND PHYSICS
(2023)
Article
Materials Science, Multidisciplinary
Sayantan Mondal, Amlan Dutta
Summary: This study presents a strategy based on Bayesian optimization to produce simulated nanocrystalline samples by minimizing the relative error between the computed and targeted grain boundary energy. Analysis of the optimized samples reveals that an increase in the average grain boundary energy primarily results from an increase in the fractions of grain boundary atoms with very low and very high free volumes.
Article
Materials Science, Multidisciplinary
S. Starikov, A. Abbass, R. Drautz, M. Mrovec
Summary: This study investigates temperature-induced disordering transitions of grain boundaries in body-centered cubic metals using classical atomistic simulations. The study reveals that gradual heating leads to continuous disordering of the grain boundary structure, accompanied by two complexion transitions, analogous to transitions described by the Berezinskii-Kosterlitz-Thouless-Halperin-Nelson-Young theory.
Article
Engineering, Mechanical
Yan Wang
Summary: The reliability and design optimization of cyber-physical-social systems (CPSS) are current key challenges, which can be optimized using trust quantification methods and ability and benevolence metrics.
JOURNAL OF MECHANICAL DESIGN
(2021)
Article
Computer Science, Interdisciplinary Applications
Leshi Shu, Ping Jiang, Yan Wang
Summary: This work proposes a multi-fidelity Bayesian optimization approach that utilizes hierarchical Kriging to reduce optimization costs, quantifies the impact of high and low-fidelity samples based on expected further improvement, and introduces a novel acquisition function to determine the location and fidelity level of the next sample simultaneously. The proposed approach is compared with state-of-the-art methods for multi-fidelity global optimization and shows that it can achieve global optimal solutions with reduced computational costs.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Mechanical
Anh Tran, Michael Eldred, Scott McCann, Yan Wang
Summary: The study introduces a novel multi-objective optimization framework, which combines three different Gaussian processes to solve multi-objective optimization problems, and employs a multi-objective augmented Tchebycheff function to convert multi-objective to single-objective at each iteration.
JOURNAL OF MECHANICAL DESIGN
(2022)
Article
Materials Science, Multidisciplinary
Jesse M. Sestito, Tequila A. L. Harris, Yan Wang
Summary: This study developed a new dimensionality reduction scheme for kMC diffusion models, where the two-dimensional diffusion model for porous microstructures was reduced to one-dimensional ones through calibration of model parameters with multi-objective Bayesian optimization. The reduced-order diffusion model showed a 675-fold faster computation compared to the original two-dimensional model.
COMPUTATIONAL MATERIALS SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Yanglong Lu, Yan Wang
Summary: Periodic surface modeling is used to optimize the shape and topology of metamaterials, reducing search space and computational cost. Mixed-integer Bayesian optimization method and population-based genetic algorithms are applied to solve the structural optimization problem, resulting in the design of mechanical metamaterials with high strength-weight ratio and negative Poisson's ratio.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Anh Tran, Tim Wildey, Jing Sun, Dehao Liu, Yan Wang
Summary: Integrated computational materials engineering (ICME) models have been crucial for accelerating materials design process, but are computationally expensive. To overcome this bottleneck, we propose a continuous-time stochastic process model using a non-linear Langevin equation to describe the evolution of statistical microstructure descriptors. We discuss calibration of the Fokker-Planck equation for these descriptors and demonstrate its applicability in three ICME models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2022)
Article
Materials Science, Multidisciplinary
Jesse M. Sestito, Michaela Kempner, Tequila A. L. Harris, Eva Zarkadoula, Yan Wang
Summary: This study aims to understand the structure-property relationships of scandium-doped aluminum nitride and presents a robust force field calibration method using a scalable multi-objective Bayesian optimization approach. By optimizing different objectives, the development of molecular dynamics force fields for aluminum scandium nitride is successfully achieved.
Article
Engineering, Manufacturing
Zhehao Zhang, Luka Malashkhia, Yi Zhang, Eduard Shevtshenko, Yan Wang
Summary: A new seam prediction method based on Gaussian process surrogate modeling is proposed in this study, and the efficiency, accuracy, and robustness of the proposed method are evaluated with welding experiments.
JOURNAL OF MANUFACTURING PROCESSES
(2022)
Article
Physics, Applied
Omar Hussein, D. Keith Coffman, Khalid Hattar, Eric Lang, Shen J. Dillon, Fadi Abdeljawad
Summary: In this study, we demonstrate a morphological instability where a polycrystalline nanorod breaks up into isolated domains at grain boundaries. The destabilizing role of grain boundaries is shown, with the critical wavelength for the instability decreasing as the grain boundary energy increases. Phase field simulations predict the temporal evolution of interfacial profiles in quantitative agreement with experimental observations.
APPLIED PHYSICS LETTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Luka Malashkhia, Dehao Liu, Yanglong Lu, Yan Wang
Summary: Physics-constrained Bayesian neural network (PCBNN) framework is proposed to quantify the uncertainty in neural networks by considering both bias and variance of predictions. Variance and Kullback-Leibler divergence of neural network parameters are incorporated in the total loss function, and the weights associated with different losses are adjusted adaptively. The training of PCBNNs is formulated as solving a minimax problem. Engineering examples of heat transfer and phase transition demonstrate the effectiveness of PCBNN in improving prediction accuracy and precision.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Dehao Liu, Pranav Pusarla, Yan Wang
Summary: Data sparsity remains the main challenge of applying machine learning models for complex scientific and engineering problems. A new scheme of multifidelity physics-constrained neural networks with minimax architecture is proposed to improve the data efficiency of training neural networks by incorporating physical knowledge as constraints and sampling data with various fidelities. The framework combines fully connected neural networks with two levels of fidelities to improve prediction accuracy.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Rio Parsons, Jesse M. Sestito, Bethany S. Luke
Summary: In this study, the isolated effects of fiber alignment, diameter, and spacing on cell morphology and migration were analyzed, and optimal combinations of fiber diameter and spacing were identified to promote cell elongation and migration, potentially improving outcomes after tendon or ligament rupture.
Article
Engineering, Manufacturing
Dehao Liu, Yan Wang
Summary: The high-dimensionality of the process-structure-property relationships in metal additive manufacturing poses a significant research challenge for process optimization. This study proposes a hybrid physics-based data-driven process design framework that utilizes mesoscale multiphysics simulation, physics-constrained neural networks, and Bayesian optimization to establish reliable process-structure surrogates. By optimizing the initial temperature and cooling rate, the desired dendritic area and microsegregation level are achieved, demonstrating the effectiveness of the framework.
MANUFACTURING LETTERS
(2022)
Correction
Materials Science, Multidisciplinary
A. D. Boccardo, M. Tong, S. B. Leen, D. Tourret, J. Segurado
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Tao Li, Qing Hou, Jie-chao Cui, Jia-hui Yang, Ben Xu, Min Li, Jun Wang, Bao-qin Fu
Summary: This study investigates the thermal and defect properties of AlN using molecular dynamics simulation, and proposes a new method for selecting interatomic potentials, developing a new model. The developed model demonstrates high computational accuracy, providing an important tool for modeling thermal transport and defect evolution in AlN-based devices.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Shin-Pon Ju, Chao-Chuan Huang, Hsing-Yin Chen
Summary: Amorphous boron nitride (a-BN) is a promising ultralow-dielectric-constant material for interconnect isolation in integrated circuits. This study establishes a deep learning potential (DLP) for different forms of boron nitride and uses molecular dynamics simulations to investigate the mechanical behaviors of a-BN. The results reveal the structure-property relationships of a-BN, providing useful insights for integrating it in device applications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
M. Salman, S. Schmauder
Summary: Shape memory polymer foams (SMPFs) are lightweight cellular materials that can recover their undeformed shape through external stimulation. Reinforcing the material with nano-clay filler improves its physical properties. Multiscale modeling techniques can be used to study the thermomechanical response of SMPFs and show good agreement with experimental results.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Laura Gueci, Francesco Ferrante, Marco Bertini, Chiara Nania, Dario Duca
Summary: This study investigates the acidity of 30 Bronsted sites in the beta-zeolite framework and compares three computational methods. The results show a wide range of deprotonation energy values, and the proposed best method provides accurate calculations.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
K. A. Lopes Lima, L. A. Ribeiro Junior
Summary: Advancements in nanomaterial synthesis and characterization have led to the discovery of new carbon allotropes, including biphenylene network (BPN). The study finds that BPN lattices with a single-atom vacancy exhibit higher CO2 adsorption energies than pristine BPN. Unlike other 2D carbon allotropes, BPN does not exhibit precise CO2 sensing and selectivity by altering its band structure configuration.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Jay Kumar Sharma, Arpita Dhamija, Anand Pal, Jagdish Kumar
Summary: In this study, the quaternary Heusler alloys LiAEFeSb were investigated for their crystal structure, electronic properties, and magnetic behavior. Density functional theory calculations revealed that LiSrFeSb and LiBaFeSb exhibit half-metallic band structure and 100% spin polarization, making them excellent choices for spintronic applications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Roman A. Eremin, Innokentiy S. Humonen, Alexey A. Kazakov, Vladimir D. Lazarev, Anatoly P. Pushkarev, Semen A. Budennyy
Summary: Computational modeling of disordered crystal structures is essential for studying composition-structure-property relations. In this work, the effects of Cd and Zn substitutions on the structural stability of CsPbI3 were investigated using DFT calculations and GNN models. The study achieved accurate energy predictions for structures with high substitution contents, and the impact of data subsampling on prediction quality was comprehensively studied. Transfer learning routines were also tested, providing new perspectives for data-driven research of disordered materials.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Zhixin Sun, Hang Dong, Yaohui Yin, Ai Wang, Zhen Fan, Guangyong Jin, Chao Xin
Summary: In this study, the crystal structure, electronic structure, and optical properties of KH2PO4: KDP crystals under different pressures were investigated using the generalized gradient approximate. It was found that high pressure caused a phase transition in KDP and greatly increased the band gap. The results suggest that high pressure enhances the compactness of KDP and improves the laser damage threshold.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Tingting Yu
Summary: This study presents atomistic simulations revealing that an increase in driving force may result in slower grain boundary movement and switches in the mode of grain boundary shear coupling migration. Shear coupling behavior is found to effectively alleviate stress and holds potential for stress relaxation and microstructure manipulation in materials.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Y. Zhang, X. Q. Deng, Q. Jing, Z. S. Zhang
Summary: The electronic properties of C2N/antimonene van der Waals heterostructure are investigated using density functional theory. The results show that by applying horizontal strain, vertical strain, electric field, and interlayer twist, the electronic structure can be adjusted. Additionally, the band alignment and energy states of the heterostructure can be significantly changed by applying vertical strain on the twisted structure. These findings are important for controlling the electronic properties of heterostructures.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Chad E. Junkermeier, Evan Larmand, Jean-Charles Morais, Jedediah Kobebel, Kat Lavarez, R. Martin Adra, Jirui Yang, Valeria Aparicio Diaz, Ricardo Paupitz, George Psofogiannakis
Summary: This study investigates the adsorption properties of carbon dioxide (CO2), methane (CH4), and dihydrogen (H2) in carbophenes functionalized with different groups. The results show that carbophenes can be promising adsorbents for these gases, with high adsorption energies and low desorption temperatures. The design and combination of functional groups can further enhance their adsorption performance.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Y. Borges, L. Huber, H. Zapolsky, R. Patte, G. Demange
Summary: Grain boundary structure is closely related to solute atom segregation, and machine learning can predict the segregation energy density. The study provides a fresh perspective on the relationship between grain boundary structure and segregation properties.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
M. R. Jones, L. T. W. Fey, I. J. Beyerlein
Summary: In this work, a three-dimensional ab-initio informed phase-field-dislocation dynamics model combined with Langevin dynamics is used to investigate glide mechanisms of edge and screw dislocations in Nb at finite temperatures. It is found that the screw dislocation changes its mode of glide at two distinct temperatures, which coincides with the thermal insensitivity and athermal behavior of Nb yield strengths.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Joshua A. Vita, Dallas R. Trinkle
Summary: This study introduces a new machine learning model framework that combines the simplicity of spline-based potentials with the flexibility of neural network architectures. The simplified version of the neural network potential can efficiently describe complex datasets and explore the boundary between classical and machine learning models. Using spline filters for encoding atomic environments results in interpretable embedding layers that can incorporate expected physical behaviors and improve interpretability through neural network modifications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
