Characterization, modeling, and simulation of multiscale directed-assembly systems

Nanoscience is a rapidly developing field at the nexus of all physical sciences which holds the potential for mankind to gain a new level of control of matter over matter and energy altogether. Directed-assembly is an emerging field within nanoscience in which non-equilibrium system dynamics are controlled to produce scalable, arbitrarily complex and interconnected multi-layered structures with custom chemical, biologically or environmentally-responsive, electronic, or optical properties. We construct mathematical models and interpret data from direct-assembly experiments via application and augmentation of classical and contemporary physics, biology, and chemistry methods. Crystal growth, protein pathway mapping, LASER tweezers optical trapping, and colloid processing are areas of directed-assembly with established experimental techniques. We apply a custom set of characterization, modeling, and simulation techniques to experiments to each of these four areas. Many of these techniques can be applied across several experimental areas within directed-assembly and to systems featuring multiscale system dynamics in general. We pay special attention to mathematical methods for bridging models of system dynamics across scale regimes, as they are particularly applicable and relevant to directed-assembly. We employ massively parallel simulations, enabled by custom software, to establish underlying system dynamics and develop new device production methods

Electron-beam-assisted superplastic shaping of nanoscale amorphous silica

At room temperature, glasses are known to be brittle and fracture upon deformation. Zheng et al. show that, by exposing amorphous silica nanostructures to a low-intensity electron beam, it is possible to achieve dramatic shape changes, including a superplastic elongation of 200% for nanowires

Nanoporous graphene as a desalination membrane : a computational study

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 19-21).With conventional water sources in short and decreasing availability, new technologies for water supply have a crucial role to play in addressing the world's clean water needs in the 21st century. In this thesis, we examine how nanometer-scale pores in single-layer freestanding graphene can effectively filter NaCl salt from water. Using classical molecular dynamics, we report the desalination performance of such membranes as a function of pore size, chemical functionalization, and applied pressure. Our results indicate that the membrane's ability to prevent the salt passage depends critically on pore diameter, with pores in the 0.7-0.9 nm range allowing for water flow while blocking ions. Further, an investigation into the role of chemical functional groups bonded to the edges of graphene pores suggests that commonly occurring hydroxyl groups can roughly double the water flux thanks to their hydrophilic character. The increase in water flux comes at the expense of less consistent salt rejection performance, which we attribute to the ability of hydroxyl functional groups to substitute for water molecules in the hydration shell of the ions. Overall, our results indicate that the water permeability of this material is several orders of magnitude higher than conventional reverse osmosis membranes, and that nanoporous graphene may have a valuable role to play for water purification.by David H. Cohen-Tanugi.S.M

Compilation of Abstracts for SC12 Conference Proceedings

1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets

Insight to the Liquid Vapor Phase Change Processes Using Molecular Dynamics Method.

PhD Theses.In this thesis, condensation process has been first addressed. The dependences of formation and transition of condensation mode on surface wettability ( ) and temperature difference T ) is explored . F ive formation mechanisms and two transition mechanisms are revealed. Meanwhile, the dependence of nano confined surface condensation on tangentially external force field ( f e ) is considered. The dynamic behaviours of nano confined surface condensation are analysed. The heat transfer analysis shows that f e indirectly influences the interfacial thermal resistance by direct influence on condensation. Results also show that as f e increases or decreases, the dissipated heats increase and gradually take over the total transferred heat, which finally reduces or suppresses the occurrence of condensation. Furthermore, the growth and self jumping of single droplet on the nanostructured surfaces are systematically investigated. The MD simulation has proved the effectiveness of self jumping of single nanodroplet driven by Laplace pressure. The results show tha t increasing the surface wettability and size of the pinning site promotes the droplet growth but blocks the droplet self jumping. Secondly, this thesis has focused on the liquid to vapor process . How to maintain and enhance nanofilm evaporation on nanopi llar surfaces are systematically investigated. The results show that the smaller pitch between nanopillars and larger diameter of nanopillars can enhance evaporation but also raise the possibility of boiling, whereas the smaller height of nanopillars can e nhance evaporation and suppress boiling. T he nanofilm thickness should be maintained beyond a threshold to avoid the suppression effect of disjoining pressure on evaporation. Additionally, the generation and evolution of nanobubbles on nanoparticles ( are studied. The results demonstrates that the superhydrophobic GNP is favourable Abstract V for fast and energy for fast and energy--saving nanobubble generation. saving nanobubble generation. Increasing Increasing heating intensity (Q) can can promote the generation and growth of nanobubbles for a given promote the generation and growth of nanobubbles for a given . . The maximum radius of The maximum radius of the nanobubble is found to be dependent on the nanobubble is found to be dependent on and not Q

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

[EN] CONSPECTUS: Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires draWing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the "missing link" needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new "ab initio" organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.This work has been supported by the EU through ERC-AdG2014-671093, by the Spanish Government through SEV-20160683 and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE), and by La Caixa-Foundation through MIT -SPAIN MISTI program (LCF/PR/MIT17/11820002). Y.R.-L. thanks the DoE for funding through the Office of Basic Energy Sciences (DE-SC0016214).Moliner Marin, M.; Román-Leshkov, Y.; Corma Canós, A. (2019). Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Accounts of Chemical Research. 52(10):2971-2980. https://doi.org/10.1021/acs.accounts.9b00399S29712980521

Development and analysis of Tinker-OpenMM as a GPU-based free energy perturbation engine

The utilization of computational technologies for the lead optimization process is one of the biggest challenges in the computational chemistry field. In this dissertation, I describe the addition of GPU-based absolute and relative free energy calculation methods using polarizable force field AMOEBA to Tinker-OpenMM. I then proceed to test the capabilities of this platform by studying the binding free energy and binding structures of derivatives of the MELK inhibitor IN17. Also, I present the implementation of virial-based pressure control to the Tinker-OpenMM platform that is needed for performing isobaric simulations.Cellular and Molecular Biolog

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

A model for polymer membranes

Separation processes are widely used throughout the chemical and pharmaceutical industries. Polymer membranes have the potential to significantly improve both energy usage and the costs of separation processes by reducing reliance on fractional distillation. For this to occur methods to control the porosity of the polymer membranes must be identified. The P84 molecule is a relatively complex co-polymer with numerous strongly interacting rigid groups, with a persistence length of over 1.1 nm, and the region in which filtration pores form in the membrane is typically 50–80nm thick, whilst the pores of interest within the membrane are typically less than 0.5 nm in size. P84 membranes are used commercially to separate molecules from organic solvents, in a process called organic solvent nanofiltration. Recent experiments with membranes produced from the P84 polyimide molecule found that altering the solvent used in the initial stage of manufacture radically altered the size of the sub-nanometre pores in the filtration region of the membrane. This effect was not expected, and could not be explained by the available models for polymer membrane formation. I present here a model as well as key results developed during my investigation of the formation of P84 polymer membranes. The model uses a mixture of fully atomistic molecular dynamics simulations of a single P84 molecule in solvent and coarse grained Monte Carlo simulations containing hundreds of complete polymer molecules. It demonstrates that the experimentally observed changes in pore sizes in P84 membranes can be explained by the differing interaction energies between the solvents and the polymers. I further present a new method for coarse graining aromatic polymers in molecular dynamics simulations which has been shown to permit the time step to be increased from 1 fs to 5 fs whilst maintaining all-atom accuracy.Open Acces