Extended Ensemble Molecular Dynamics for Thermodynamics of Phases

The first-order phase transitions and related thermodynamics properties are primary concerns of materials sciences and engineering. In traditional atomistic simulations, the phase transitions and the estimation of their thermodynamic properties are challenging tasks because the trajectories get trapped in local minima close to the initial states. In this study, we investigate various extended ensemble molecular dynamics (MD) methods based on the multicanonical ensemble method using the Wang-Landau (WL) approach. We performed multibaric-multithermal (MBMT) method to fluid phase, gas-liquid transition, and liquid-solid transition of the Lennard-Jones (LJ) system. The derived thermodynamic properties of the fluid phase and the gas-liquid transition from the MBMT agree well with the previously reported equation of states (EOSs). However, the MBMT cannot correctly predict the liquid-solid transition. The multiorder-multithermal (MOMT) ensemble shows significantly enhanced sampling between liquid and solid states with an accurate estimation of transition temperatures. We further investigated the dynamics of each system based on their free energy shapes, providing fundamental insights for their sampling behaviors. This study guides the prediction of broader crystalline materials, e.g., alloys, for their phases and thermodynamic properties from atomistic modeling

Mechanics of mineralized collagen fibrils upon transient loads

Collagen is a key structural protein in the human body, which undergoes mineralization during the formation of hard tissues. Earlier studies have described the mechanical behavior of bone at different scales highlighting material features across hierarchical structures. Here we present a study that aims to understand the mechanical properties of mineralized collagen fibrils upon tensile/compressive transient loads, investigating how the kinetic energy propagates and it is dissipated at the molecular scale, thus filling a gap of knowledge in this area. These specific features are the mechanisms that Nature has developed to passively dissipate stress and prevent structural failures. In addition to the mechanical properties of the mineralized fibrils, we observe distinct nanomechanical behaviors for the two regions (i.e., overlap and gap) of the D-period to highlight the effect of the mineralization. We notice decreasing trends for both wave speeds and Young s moduli over input velocity with a marked strengthening effect in the gap region due to the accumulation of the hydroxyapatite. In contrast, the dissipative behavior is not affected by either loading conditions or the mineral percentage, showing a stronger dampening effect upon faster inputs compatible to the bone behavior at the macroscale. Our results improve the understanding of mineralized collagen composites unveiling the energy dissipative behavior of such materials. This impacts, besides the physiology, the design and characterization of new bioinspired composites for replacement devices (e.g., prostheses for sound transmission or conduction) and for optimized structures able to bear transient loads, e.g., impact, fatigue, in structural applications

The mechanics and design of a lightweight three-dimensional graphene assembly

Recent advances in three-dimensional (3D) graphene assembly have shown how we can make solid porous materials that are lighter than air. It is plausible that these solid materials can be mechanically strong enough for applications under extreme conditions, such as being a substitute for helium in filling up an unpowered flight balloon. However, knowledge of the elastic modulus and strength of the porous graphene assembly as functions of its structure has not been available, preventing evaluation of its feasibility. We combine bottom-up computational modeling with experiments based on 3D-printed models to investigate the mechanics of porous 3D graphene materials, resulting in new designs of carbon materials. Our study reveals that although the 3D graphene assembly has an exceptionally high strength at relatively high density (given the fact that it has a density of 4.6% that of mild steel and is 10 times as strong as mild steel), its mechanical properties decrease with density much faster than those of polymer foams. Our results provide critical densities below which the 3D graphene assembly starts to lose its mechanical advantage over most polymeric cellular materials.United States. Office of Naval Research (Grant No. N00014-16-1-233)United States. Air Force. Office of Scientific Research (Multidisciplinary University Research Initiative Grant No. FA9550-15-1-0514)ASF-NOR

Molecular origin of viscoelasticity in mineralized collagen fibrils

Bone is mineralized tissue constituting the skeletal system, supporting and protecting body organs and tissues. At the molecular level, mineralized collagen fibril is the basic building block of bone tissue, and hence, understanding bone properties down to fundamental tissue structures enables to better identify the mechanisms of structural failures and damages. While efforts have focused on the study of the micro- and macro-scale viscoelasticity related to bone damage and healing based on creep, mineralized collagen has not been explored on a molecular level. We report a study that aims at systematically exploring the viscoelasticity of collagenous fibrils with different mineralization levels. We investigate the dynamic mechanical response upon cyclic and impulsive loads to observe the viscoelastic phenomena from either shear or extensional strains via molecular dynamics. We perform a sensitivity analysis with several key benchmarks: intrafibrillar mineralization percentage, hydration state, and external load amplitude. Our results show a growth of the dynamic moduli with an increase of mineral percentage, pronounced at low strains. When intrafibrillar water is present, the material softens the elastic component but considerably increases its viscosity, especially at high frequencies. This behaviour is confirmed from the material response upon impulsive loads, in which water drastically reduces the relaxation times throughout the input velocity range by one order of magnitude, with respect to the dehydrated counterparts. We find that upon transient loads, water has a major impact on the mechanics of mineralized fibrillar collagen, being able to improve the capability of the tissue to passively and effectively dissipate energy, especially after fast and high-amplitude external loads

Sub-Nanometer Channels Embedded in Two-Dimensional Materials

Two-dimensional (2D) materials are among the most promising candidates for next-generation electronics due to their atomic thinness, allowing for flexible transparent electronics and ultimate length scaling. Thus far, atomically-thin p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have been demonstrated. While 2D materials achieve the thinnest possible devices, precise nanoscale control over the lateral dimensions is also necessary. Here, we report the direct synthesis of sub-nanometer-wide 1D MoS2 channels embedded within WSe2 monolayers, using a dislocation-catalyzed approach. The 1D channels have edges free of misfit dislocations and dangling bonds, forming a coherent interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics simulations, we have identified other combinations of 2D materials where 1D channels can also be formed. The electronic band structure of these 1D channels offer the promise of carrier confinement in a direct-gap material and charge separation needed to access the ultimate length scales necessary for future electronic applications.Comment: 22 pages main manuscript and methods, 4 main figures, 30 pages supplementary materials, 16 extended figure

Phenotyping of rice in salt stress environment using high-throughput infrared imaging

Phenotyping of rice (Oryza sativa L. cv. Donggin) in salt stress environment using infrared imaging was conducted. Results were correlated with the most frequently used physiological parameters such as stomatal conductance, relative water content and photosynthetic parameters. It was observed that stomatal conductance (R2 = –0.618) and relative water content (R2 = –0.852) were significantly negatively correlated with average plant temperature (thermal images), while dark-adapted quantum yield (Fv/Fm, R2 = –0.325) and performance index (R2 = –0.315) were not consistent with plant temperature. Advantages of infrared thermography and utilization of this technology for the selection of stress tolerance physiotypes are discussed in detail

Multiscale modeling of two-dimensional materials : structures, properties, and designs

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 257-274).Multiscale modeling undertakes to describe a system with multiple models at different scales. In principle, quantum mechanics provides sufficient information. However, the development of a scaled-up model, e.g., molecular dynamics, from quantum mechanics, should be validated against the experiments. Two-dimensional (2D) materials provide excellent platforms to verify theoretical models by directly comparing atomic structures and properties with advanced transmission electron microscopy (TEM) techniques due to their high crystallinity and thin nature. In this thesis, molecular dynamics (MD) models have been developed for the 2D transition metal dichalcogenides (TMDs) such as MoS₂, WS₂, MoSe₂, and WSe₂ from density functional theory (DFT) by focusing on their nonlinearity and failure strains. The structures, crack-tip behaviors, and fracture patterns from the models are directly compared with atomic level in-situ TEM images.The models have revealed atomic scale mechanisms on the crack-tip behaviors in the single crystals such as roles of sulfur vacancies, geometric interlocking frictions, and the directions of crack propagation. The models have been further validated with more complicated structures from grain boundaries in the WS₂ bilayer and lateral heterostructures, e.g., MoS₂-WSe₂ by the images from ADF-STEM. Also, a method for generation of grain boundary has been proposed for well-stitched grain boundaries based on experimentally observed dislocations and defects. The models and methods have been utilized to understand the chemical reactions for MoS₂ channel growth in WSe₂ and fracture toughness of polycrystalline graphene. Finally, the validated models and methods are utilized to predict the atomic structures of 2D materials with three-dimensional (3D) surfaces, e.g., triply periodic minimal surfaces (TPMS) and corrugated surfaces with non-zero Gaussian curvatures.The mechanics, failure behaviors, and thermal properties of TPMS graphene are systematically studied from the predicted structures. A recent experiment shows the predicted scaling laws of Young's modulus and strengths agree well with the measurements."Funded by the MIT Presidential Fellowship (Edward H. Linde), AFOSR (DOD-MURI, Grant No. FA9550-15-1-0514), ONR (Grant No. N00014- 16-1-233), NSF (Grant No. CMMI-1300649), and NIH (Grant No. U01EB014976; 5U01EB016422)"--Page 8by Gang Seob Jung.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Civil and Environmental Engineerin

Active Learning of Neural Network Potentials for Rare Events

Atomistic simulation with machine learning-based potentials (MLPs) is an emerging tool for understanding materials\u27 properties and behaviors and predicting novel materials. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside their training domain and often fail catastrophically in predicting rare events in molecular dynamics (MD) simulations. The rare events in atomistic modeling typically include chemical bond breaking/formation, phase transitions, and materials failure, which are critical for new materials design, synthesis, and manufacturing processes. In this study, we develop an automated active learning (AL) capability by combining NNPs and enhanced sampling methods for capturing rare events to derive NNPs for targeted applications. We develop a decision engine based on configurational similarity and uncertainty quantification (UQ), using data augmentation for effective AL loops to distinguish the informative data from enhanced sampled configurations, showing that the generated data set achieves an activation energy error of less than 1 kcal/mol. Furthermore, we have devised a strategy to alleviate training uncertainty within AL iterations through a carefully constructed data selection process that leverages an ensemble approach. Our study provides essential insight into the relationship between data and the performance of NNP for the rare event of bond breaking under mechanical loading. It highlights strategies for developing NNPs of broader materials and applications through active learning