Molecular simulations and visualization: introduction and overview

Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building

Structures and dynamics investigation of phase selection in metallic alloy systems

Different phases of metallic alloys have a wide range of applications. However, the driving mechanisms of the phase selections can be complex. For example, the detailed pathways of the phase transitions in the devitrification process still lack a comprehensive interpretation. So, the understanding of the driving mechanisms of the phase selections is very important. In this thesis, we focus on the study of the Al-Sm and other related metallic alloy systems by simulation and experiment. A procedure to evaluate the free energy has been developed within the framework of thermodynamic integration, coupled with extensive GPU-accelerated molecular dynamics (MD) simulations; The ``spatially-correlated site occupancy\u27\u27 has been observed and measured in the $\epsilon$-Al$_{60}$Sm$_{11}$ phase. Contrary to the common belief that nonstoichiometry is often the outcome of the interplay of enthalpy of formation and configurational entropy at finite temperatures, our results from Monte Carlo (MC) and molecular dynamics (MD) simulations, imply that kinetic effects, especially the limited diffusivity of Sm is crucial for the appearance of the observed spatial correlations in the nonstoichiometric $\epsilon$ phase. Moreover, in order to overcome the time limitation in MD simulation of the nucleation process, a ``persistent-embryo method\u27\u27 has been developed, which opens a new avenue to study solidification under realistic experimental conditions via atomistic computer simulation. Based on this thesis study, we have achieved deeper understanding of the driving mechanisms of the phase selections, and laid a foundation for further prediction and control of the fabrication of novel metallic alloy materials. This thesis consists of the following seven chapters: Chapter 1 briefly introduces the history of the development of the metallic alloy materials, and their significant impact on human civilization. In particular, the research background of metallic glasses has been reviewed, and some unsolved questions have been raised. Chapter 2 is the literature review. In the first section of it, simulation methods, including molecular dynamics (MD) simulation, Monte Carlo method (MC) simulation, and related technical issues in the computer simulation to mimic the real system have been reviewed. In the second section of it, analysis methods, including structural and dynamical analysis methods, classical nucleation theory, free energy computing algorithms, and experimental techniques have been reviewed. Chapter 3 reports our work in a self-contained procedure to evaluate the free energy of liquid and solid phases of an alloy system. We start from the Einstein crystal as the reference system, using thermodynamic integration to solve the free energy of a single-element solid phase. Then we compute the free energy difference between the solid and liquid phases using Gibbs-Duhem integration. After that we construct an ``alchemical\u27\u27 path connecting a pure liquid and a liquid alloy to calculate the mixing enthalpy and entropy. This procedure is of great importance because the evaluation of free energy is fundamental to achieving microscopic understandings of freezing and melting phenomena. Chapter 4 elucidates the origin of the spatially-correlated site occupancy in the non-stoichiometric meta-stable $\epsilon$-Al$_{60}$Sm$_{11}$ phase. This STEM observed spatially-correlated site occupancy cannot be explained by the ``average crystal\u27\u27 description from Rietveld analysis of diffraction data, or by the lowest free energy structure established in MC simulations. MD simulations of the growth of $\epsilon$-Al$_{60}$Sm$_{11}$ in undercooled liquid show that when the diffusion range of Sm is limited to $\sim 4\AA$, the correlation function of the as-grown crystal structure agrees well with that of the STEM images. Conclusion thus has been made that the kinetic effects, especially the limited diffusivity of Sm atoms plays an important role in determining the non-stoichiometric site occupancy. In addition to the free energy point of view, this result helps us to have a deeper understanding of phase selections from structural and dynamical points of views. Chapter 5 describes the ``persistent-embryo\u27\u27 method (PEM) nucleation simulation. The PEM is developed to facilitate crystal nucleation in MD simulations by preventing small crystal embryos from melting using external spring forces, so that the early state of rare nucleation events can be accessed. This method opens a new avenue to study solidification under realistic experimental conditions. The nucleation rates of pure Ni, and of B2 phase of glass former Cu-Zr alloy have been computed using PEM. We also apply PEM to the Al-Sm system to study the nucleation of $\epsilon$-Al$_{60}$Sm$_{11}$ phase in the undercooled Al-Sm liquid, complex and interesting behaviors, different from the Ni case, have been found. Chapter 6 presents an implementation of EAM and FS inter-atomic potentials in HOOMD-blue, a GPU software designed to perform classical molecular dynamic simulations. The accuracy of the code has been verified in a variety of broad tests, the performance of the code is significantly faster than LAMMPS running on a typical CPU cluster. Furthermore, our hoomd.metal module follows HOOMD-blue code convention, which allows it to be coupled to the extensive python libraries. This package makes the MD simulations in the thesis and other related fields faster and more convenient. Chapter 7 is a summary of my Ph.D. thesis study, and proposes a plan for future works

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

Perspective on Coarse-Graining, Cognitive Load, and Materials Simulation

The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory. For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice

Understanding Self-Assembly and Charge Transport in Organic Solar Cells Through Efficient Computation

Organic solar cells capable of sustainably generating electricity are possible if: (1) The structures assembled by photoactive molecules can be controlled, and (2) The structures favorable for charge transport can be determined. In this dissertation we conduct computational studies to understand relationships between organic solar cell compounds, processing, structure and charge transport. We advance tools for encapsulating computational workflows so that simulations are more reproducible and transferable. We find that molecular dynamic simulations using simplified models efficiently predict experimental structures. We find that the mobilities of charges through these structures—as determined by kinetic Monte Carlo simulations—match qualitative trends expected with molecular ordering and in some cases agree quantitatively with experimental measurements. We identify percolating clusters with overlapping pi-orbitals as vital for fast charge transport, which are achieved through polymer tie-chains and extended molecular stacking. We find that machine learning predictions of electronic couplings from quantum chemical calculations gives a two-order-of-magnitude speed improvement relating structure to charge transport versus repeating the quantum calculations. We identify limitations of our structural and charge transport predictions, and provide recommendations for advancing future investigations of organic solar cells. In sum, the computational tools developed and employed herein enable the most broad and experimentally-validated sampling of self-assembled structure as a function of chemistry and processing to date. The fundamental understanding gained from these simulations informs the self-assembly and structure-transport relationships needed to advance organic solar cell engineering

Efficient periodic resolution-of-the-identity Hartree–Fock exchange method with k-point sampling and Gaussian basis sets

Simulations of condensed matter systems at the hybrid density functional theory level pose significant computational challenges. The elevated costs arise from the non-local nature of the Hartree–Fock exchange (HFX) in conjunction with the necessity to approach the thermodynamic limit. In this work, we address these issues with the development of a new efficient method for the calculation of HFX in periodic systems, employing k-point sampling. We rely on a local atom-specific resolution-of-the-identity scheme, the use of atom-centered Gaussian type orbitals, and the truncation of the Coulomb interaction to limit computational complexity. Our real-space approach exhibits a scaling that is, at worst, linear with the number of k-points. Issues related to basis set diffuseness are effectively addressed through the auxiliary density matrix method. We report the implementation in the CP2K software package, as well as accuracy and performance benchmarks. This method demonstrates excellent agreement with equivalent Γ-point supercell calculations in terms of relative energies and nuclear gradients. Good strong and weak scaling performances, as well as graphics processing unit (GPU) acceleration, make this implementation a promising candidate for high-performance computing

Graphics Processing Unit Accelerated Coarse-Grained Protein-Protein Docking

Graphics processing unit (GPU) architectures are increasingly used for general purpose computing, providing the means to migrate algorithms from the SISD paradigm, synonymous with CPU architectures, to the SIMD paradigm. Generally programmable commodity multi-core hardware can result in significant speed-ups for migrated codes. Because of their computational complexity, molecular simulations in particular stand to benefit from GPU acceleration. Coarse-grained molecular models provide reduced complexity when compared to the traditional, computationally expensive, all-atom models. However, while coarse-grained models are much less computationally expensive than the all-atom approach, the pairwise energy calculations required at each iteration of the algorithm continue to cause a computational bottleneck for a serial implementation. In this work, we describe a GPU implementation of the Kim-Hummer coarse-grained model for protein docking simulations, using a Replica Exchange Monte-Carlo (REMC) method. Our highly parallel implementation vastly increases the size- and time scales accessible to molecular simulation. We describe in detail the complex process of migrating the algorithm to a GPU as well as the effect of various GPU approaches and optimisations on algorithm speed-up. Our benchmarking and profiling shows that the GPU implementation scales very favourably compared to a CPU implementation. Small reference simulations benefit from a modest speedup of between 4 to 10 times. However, large simulations, containing many thousands of residues, benefit from asynchronous GPU acceleration to a far greater degree and exhibit speed-ups of up to 1400 times. We demonstrate the utility of our system on some model problems. We investigate the effects of macromolecular crowding, using a repulsive crowder model, finding our results to agree with those predicted by scaled particle theory. We also perform initial studies into the simulation of viral capsids assembly, demonstrating the crude assembly of capsid pieces into a small fragment. This is the first implementation of REMC docking on a GPU, and the effectuate speed-ups alter the tractability of large scale simulations: simulations that otherwise require months or years can be performed in days or weeks using a GPU

Porting of DSMC to multi-GPUs using OpenACC

The Direct Simulation Monte Carlo has become the method of choice for studying gas flows characterized by variable rarefaction and non-equilibrium effects, rising interest in industry for simulating flows in micro-, and nano-electromechanical systems. However, rarefied gas dynamics represents an open research challenge from the computer science perspective, due to its computational expense compared to continuum computational fluid dynamics methods. Fortunately, over the last decade, high-performance computing has seen an exponential growth of performance. Actually, with the breakthrough of General-Purpose GPU computing, heterogeneous systems have become widely used for scientific computing, especially in large-scale clusters and supercomputers. Nonetheless, developing efficient, maintainable and portable applications for hybrid systems is, in general, a non-trivial task. Among the possible approaches, directive-based programming models, such as OpenACC, are considered the most promising for porting scientific codes to hybrid CPU/GPU systems, both for their simplicity and portability. This work is an attempt to port a simplified version of the fm dsmc code developed at FLOW Matters Consultancy B.V., a start-up company supporting this project, on a multi-GPU distributed hybrid system, such as Marconi100 hosted at CINECA, using OpenACC. Finally, we perform a detailed performance analysis of our DSMC application on Volta (NVIDIA V100 GPU) architecture based computing platform as well as a comparison with previous results obtained with x64 86 (Intel Xeon CPU) and ppc64le (IBM Power9 CPU) architectures

From Experimental Studies to Coarse-Grained Modeling: Characterization of Surface Area to Volume Ratio Effects on the Swelling of Poly (Ethylene Glycol) Dimethacrylate Hydrogels

Understanding the performance of widely applied nanoscale hydrogel biomaterials is an unmet need within the biomedical field. The objective of this master’s thesis project was to evaluate the effects size and surface area has on the in vivo behavior of nanoscale hydrogels. The hypothesis tested was that at the nanoscale, the increased surface area to volume effects of nanoscale hydrogels play and important role in the overall swelling of hydrogels, such that nanoscale hydrogels swell to a greater degree than their bulk counterparts. To investigate this, the bulk swelling behavior of a series of neutral poly (ethylene glycol) di-methacrylate (PEGDMA) hydrogels was experimentally tested. Along with experimental studies, a computational model based on the experimental findings was developed to serve as a means of predicting nanoscale swelling and subsequent drug release behavior. The computational hydrogel model was validated with the experimental densities and swelling ratios calculated. The surfaces of swollen hydrogels had a density gradient until reaching a stabilized, core density. As the size of the hydrogel decreases, the surface area to volume ratio increases, which enhances surface effects for micro- and nanoscale hydrogels. This conclusion helps to confirm the hypothesis that the increased surface area to volume ratio of nanoscale hydrogels affects the overall swelling ratio in comparison to their bulk counter parts. Particle size should be considered when characterizing nanoscale hydrogels. In this thesis, a computational hydrogel model capable of simulating hydrogel swelling for hydrogels with a dry state diameter of 40 nm was created. In the future, this model would ideally be able to simulate hydrogels with a dry state diameter ≥ 100 nm to test the full range of nanoscale size effects on hydrogel swelling