Distribution and Transport of CO 2 in Hyperbranched Poly(ethylenimine)-Loaded MCM-41: A Molecular Dynamics Simulation Approach
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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SurGBSA: Learning Representations from Molecular Dynamics Simulations
Source code implementation for "SurgGBSA: Learning Representations From Molecular Dynamics Trajectories".
Jones, WilliamD↗
Large-Scale Molecular Dynamics Simulation of the Dehydration of a Suspension of Smectite Clay Nanoparticles
Fine-grained sediments and sedimentary rocks play important roles in a variety of modern energy technologies from petroleum geology to geological carbon sequestration and radioactive waste management. However, despite their utility and ubiquity, many of their properties remain poorly understood. In particular, the ability to predict the permeability and mechanics of these media remains a persistent fundamental challenge in the geosciences. In the present work, we show how large-scale classical molecular dynamics (MD) simulations can help interpret the properties of fine-grained sedimentary material. All-atom simulations containing 30 discrete clay particles are utilized to understand the evolution of a clay nanoparticle suspension during its progressive dehydration. Microstructural (pore size distribution, tortuosity, anisotropy), thermodynamic (enthalpy and free energy of hydration, anion exclusion), mechanical (total suction), and transport properties (diffusion coefficient tensors of water and sodium) are calculated and compared to the experiment. Overall, our results provide new insight into the coupled chemistry, mechanics, and transport properties of disordered nanoparticle assemblages and shed light upon the important role of water films in controlling these properties.
02 PETROLEUM↗
Manipulating molecular order in nematic liquid crystal capillary bridges via surfactant adsorption: guiding principles from dissipative particle dynamics simulations
The ability of liquid crystals (LCs) to change orientational order is used in applications, ranging from sensors to displays. The aim of this work is to computationally investigate how surfactant adsorption on cylindrical LC bridges can be used to control such orientational order. Building from classical fundamental lessons, understanding the ordering of mesogens along a preferred axis with the help of molecular modelling contributes to investigations of systems that could be a platform for LC-based sensing applications. The coarse-grained dissipative particle dynamics (DPD) simulation method is implemented here, because it allows us to quantify the effect of molecular features on the properties of meso-scopic systems containing LC bridges, an aqueous solvent, and surfactants at various concentrations. Three surfactant types are modelled with short, medium, and long tail lengths, respectively. All surfactants adsorb at the LC–water interface. It is found that the length of the surfactant hydrophobic tail determines the effectiveness by which the LC order is affected. Short tails are not as effective as long ones. Surfactants with long tails affect the LC order, but, in agreement with experiments, predominantly only within a short distance from the LC–water interface. For these surfactants, the surface density at the LC–water interface is an important knob that can be used to control the order of the LCs. As the effective LC–surfactant interactions change, so does the distribution of the surfactants at the interface. Consistent with theoretical expectations, the results presented here elucidate the effect of molecular features on the anchoring mechanism between surfactants and mesogens within cylindrical bridges dispersed in aqueous systems and could be helpful for designing novel surface-active compounds in the development of advanced sensing devices based on LCs.
74 ATOMIC AND MOLECULAR PHYSICS↗
Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi
Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.
Gabriel Plummer↗
Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi
Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.
Gabriel Plummer↗
Ab Initio Molecular Dynamics Simulation of Divalent Metal Cation Incorporation in Calcite: Implications for Interpreting X-ray Absorption Spectroscopy Data
Calcite (CaCO 3 ) is a ubiquitous mineral with the ability to accommodate a wide range of impurities. Determination of the coordination environment and incorporation modes of impurities in calcite has historically relied primarily on the interpretation of extended X-ray absorption fine structure (EXAFS) spectroscopy. However, lack of standards combined with the large number of degrees of freedom involved in shell-by-shell fits have made the interpretation of EXAFS spectra challenging. As such, in this work, ab initio molecular dynamics (AIMD) simulations were performed to investigate the incorporation of seven divalent metal cation impurities, namely, Ba 2+ , Pb 2+ , Sr 2+ , Cd 2+ , Mn 2+ , Co 2+ , and Zn 2+ , in calcite. These cations span a wide range of sizes: 30.7 to –22.8% change in ionic radius with respect to Ca 2+ . The atomic trajectories were then used to compute EXAFS spectra for direct comparison with published experimental spectra. The simulations confirmed that all seven metal cations incorporate in calcite via substitution in the calcium site. The AIMD–EXAFS approach allowed for overcoming limitations of the shell-by-shell fitting approach, such as the difficulties in resolving weak backscatterers beyond the first coordination shell. As a result, the AIMD–EXAFS approach was able to provide a detailed and comprehensive characterization of the structural relaxation around divalent metal cation impurities in calcite.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi
The formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.
Gabriel Plummer↗
Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers
Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure–property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, we present and validate in this work the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (T g ) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for T g predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed T g predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict T g values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted T g values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure–property relationships in more complex binary and ternary copolymer systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular dynamics simulations of the first charge of a Li-ion—Si-anode nanobattery
Rechargeable lithium-ion batteries are the most popular devices for energy storage but still a lot of research needs to be done to improve their cycling and storage capacity. Silicon has been proposed as an anode material because of its large theoretical capacity of ~3600 mAh/g. Therefore, focus is needed on the lithiation process of silicon anodes where it is known that the anode increases its volume more than 300%, producing cracking and other damages. In this study, we performed molecular dynamics atomistic simulations to study the swelling, alloying, and amorphization of a silicon nanocrystal anode in a full nanobattery model during the first charging cycle. A dissolved salt of lithium hexafluorophosphate in ethylene carbonate was chosen as the electrolyte solution and lithium cobalt oxide as cathode. External electric fields are applied to emulate the charging, causing the migration of the Li-ions from the cathode to the anode, by drifting through the electrolyte solution, thus converting pristine Si gradually into Li 14 Si 5 when fully lithiated. When the electric field is applied to the nanobattery, the temperature never exceeds 360 K due to a temperature control imposed resembling a cooling mechanism. The volume of the anode increases with the amorphization of the silicon as the external field is applied by creating a layer of LiSi alloy between the electrolyte and the silicon nanocrystal and then, at the arrival of more Li-ions changing to an alloy, where the drift velocity of Li-ions is greater than the velocity in the initial nanocrystal structure. Charge neutrality is maintained by concerted complementary reduction-oxidation reactions at the anode and cathode, respectively. Also, the nanobattery model developed here can be used to study charge mobility, current density, conductance and resistivity, among several other properties of several candidate materials for rechargeable batteries and constitutes the initial point for further studies on the formation of the solid electrolyte interphase in the anode.
25 ENERGY STORAGE↗
Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. Further, this makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum chemical replica-exchange umbrella sampling molecular dynamics simulations reveal the formation mechanism of iron phthalocyanine from iron and phthalonitrile
Phthalocyanine (Pc) and its metal complexes (MPcs) have been used industrially since their discovery in the early 20th century. The phthalonitrile (PN) method is a well-known synthesis method in which Pc or MPc can be afforded by heating a mixture of PN and metal powders over 280 °C with only moderate yield. However, the formation mechanism of the phthalocyanines and the intermediate stages of this seemingly simple reaction have yet to be fully understood. To study this mechanism computationally, we carried out quantum chemical molecular dynamics (MD) simulations based on the density-functional tight-binding (DFTB) method, applying the replica-exchange umbrella sampling (REUS) method, starting from four PN molecules and one iron atom. The DFTB-REUS-MD simulations successfully yielded FePc, and a metastable structure very similar to FePc but with a reactive nitrene unit was also identified that might explain the incomplete conversion of the reactants into FePc. Furthermore, analysis of the MD trajectories reveals a three-step FePc formation mechanism for the PN method.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Isothermal Compressor Computational Fluid Dynamics Simulations (Final Report)
Carnot Compression is a startup company developing an innovative technology for air and gas compression. This technology is inherently oil-free and isothermal due to the use of water to simultaneously compress and cool the gas throughout the process. Isothermal gas compression eliminates the need to cool the compressed gas so it may lead to significant energy savings. The development of Carnot’s isothermal compressor is limited by the lack of insight into the flow and detailed behavior of the fluids (water and air/gas) inside the air end. Previous and current attempts at Computational Fluid Dynamics (CFD) simulations by Carnot have been unable to provide the level of accuracy required to use simulations for technology development. In this Cooperative Research and Development Agreement (CRADA) project, Oak Ridge National Laboratory (ORNL) used the CFD package Star-CCM+ to successfully develop a CFD simulation, providing the much-needed insight required to speed up the development. The work is intended to enable Carnot Compression to unlock its technology potential to a level sufficient for commercialization of the intended first product. The work will also prepare Carnot to scale the technology to much larger and more energy intensive applications, positioning it to broaden the product applications. Successful development of the technology has the potential to result in 20% or more efficiency gains for compression processes, or about $3 billion in annual energy savings potential for the U.S. alone.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Simulating Dynamical Phases of Chiral p + i p Superconductors with a Trapped ion Magnet
Two-dimensional p + ip superconductors and superfluids are systems that feature chiral behavior emerging from the Cooper pairing of electrons or neutral fermionic atoms with nonzero angular momentum. Their realization has been a longstanding goal because they offer great potential utility for quantum computation and memory. However, they have so far eluded experimental observation both in solid-state systems as well as in ultracold quantum gases. Here, we propose to leverage the tremendous control offered by rotating two-dimensional trapped-ion crystals in a Penning trap to simulate the dynamical phases of two-dimensional p + ip superfluids. This is accomplished by mapping the presence or absence of a Cooper pair into an effective spin-1/2 system encoded in the ions’ electronic levels. We show how to infer the topological properties of the dynamical phases, and discuss the role of beyond mean-field corrections. More broadly, our work opens the door to use trapped-ion systems to explore exotic models of topological superconductivity and also paves the way to generate and manipulate skyrmionic spin textures in these platforms.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A Method for Obtaining Liquid–Solid Adsorption Rates from Molecular Dynamics Simulations: Applied to Methanol on Pt(111) in H 2 O
Adsorption is an important step in heterogeneous catalysis as it predetermines how many reactant molecules can participate in a surface reaction per unit time. While the rate of adsorption processes is well studied in gas–solid adsorption in both theory and experiment, such rates are still not well studied for liquid–solid adsorption. This is partly because the ever-changing configurations of liquid-phase solvent molecules impede the ability to study a molecule approaching a surface from a liquid phase by either experiment or theory. In this work, we develop a method using molecular dynamics (MD) simulations to study the rate of adsorption in liquid–solid adsorption processes. Specifically, we use MD to model the diffusion of a methanol molecule in aqueous solvent and its adsorption to a Pt(111) surface. We find that by approximating the solute motion as following the same displacement rates as a random walk model, the adsorbed and non-adsorbed states of the methanol molecule near the Pt(111) surface can be discerned and quantified. In particular, this methodology enables extracting a sticking coefficient and a macroscopically relatable adsorption rate. This method can be applied to arbitrary types of reactants and surfaces, as well as different liquid environments, thus providing a general tool for predicting quantitative adsorption rates of liquid–solid adsorption systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Structural transformations of graphene exposed to nitrogen plasma: quantum chemical molecular dynamics simulations
Nitrogen-doped graphene (N-graphene) has been intensively studied for tailoring the electronic property of the graphene, because different nitrogen configurations influence the electronic properties of N-graphene in different ways. However, atomically precise control of the nitrogen configurations during the doping process remains a challenge in the synthesis of N-graphene. Moreover, additional structural transformations of the graphene carbon network structure as a side-effect of plasma doping are little understood and are as of yet uncontrollable. Therefore, we theoretically investigated the nitrogen doping process of graphene for a range of nitrogen atom incident kinetic energies in nonequilibrium quantum chemical molecular dynamics (QM/MD) simulations. We observed and characterized prominent configurations of N-containing graphene. In analogy to similar, earlier studies of graphene plasma hydrogenation, we observed an Eley–Rideal associative desorption mechanism during the graphene plasma nitrogenation, producing molecular nitrogen. Especially for graphitic-N (Gr-N) and Stone–Wales-defect-N (SW-N) configurations, which are frequently observed in experimental studies, we discovered two typical chemical reaction mechanisms which were well categorized by two key processes: adsorption of primary nitrogen dopant and collision with a secondary nitrogen dopant. Here, we discussed effects of the incident nitrogen energy on the formation mechanism, and propose a method to generate of Gr-N and SW-N configurations selectively by tuning the conditions with respect to the two key formation processes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular dynamics simulations of the secondary-binding site in disaccharide-modified glycopeptide antibiotics
Oritavancin is a semisynthetic glycopeptide antibiotic used to treat severe infections by multidrug-resistant Gram-positive pathogens. Oritavancin is known to be a thousand times more potent than vancomycin against Gram-positive bacteria due to the additional interactions with bacterial peptidoglycan (PG) facilitated by a secondary-binding site. The presence of this secondary-binding site is evident in desleucyl-oritavancin, an Edman degradation product of oritavancin, still retaining its potency against Gram-positive bacteria, whereas desleucyl-vancomycin is devoid of any antimicrobial activities. Herein, using explicit solvent molecular dynamics (MD) simulations, steered MD simulations, and umbrella sampling, we show evidence of a secondary-binding site mediated by the disaccharide-modified hydrophobic sidechain of oritavancin interactions with the pentaglycyl-bridge segment of the PG. The interactions were characterized through comparison to the interaction of PG with chloroeremomycin, vancomycin, and the desleucyl analogs of the glycopeptides. Our results show that the enhanced binding of oritavancin to PG over the binding of the other complexes studied is due to an increase in the hydrophobic effect, electrostatic and van der Waals interactions, and not the average number of hydrogen bonds. Our ranking of the binding interactions of the biomolecular complexes directly correlates with the order based on their experimental minimum inhibitory concentrations. The results of our simulations provide insight into the modification of glycopeptides to increase their antimicrobial activities or the design of novel antibiotics against pathogenic Gram-positive bacteria.
60 APPLIED LIFE SCIENCES↗
A computational fluid dynamics simulation framework for ventricular catheter design optimization
OBJECTIVE Cerebrospinal fluid (CSF) shunts are the primary treatment for patients suffering from hydrocephalus. While proven effective in symptom relief, these shunt systems are plagued by high failure rates and often require repeated revision surgeries to replace malfunctioning components. One of the leading causes of CSF shunt failure is obstruction of the ventricular catheter by aggregations of cells, proteins, blood clots, or fronds of choroid plexus that occlude the catheter’s small inlet holes or even the full internal catheter lumen. Such obstructions can disrupt CSF diversion out of the ventricular system or impede it entirely. Previous studies have suggested that altering the catheter’s fluid dynamics may help to reduce the likelihood of complete ventricular catheter failure caused by obstruction. However, systematic correlation between a ventricular catheter’s design parameters and its performance, specifically its likelihood to become occluded, still remains unknown. Therefore, an automated, open-source computational fluid dynamics (CFD) simulation framework was developed for use in the medical community to determine optimized ventricular catheter designs and to rapidly explore parameter influence for a given flow objective. METHODS The computational framework was developed by coupling a 3D CFD solver and an iterative optimization algorithm and was implemented in a high-performance computing environment. The capabilities of the framework were demonstrated by computing an optimized ventricular catheter design that provides uniform flow rates through the catheter’s inlet holes, a common design objective in the literature. The baseline computational model was validated using 3D nuclear imaging to provide flow velocities at the inlet holes and through the catheter. RESULTS The optimized catheter design achieved through use of the automated simulation framework improved significantly on previous attempts to reach a uniform inlet flow rate distribution using the standard catheter hole configuration as a baseline. While the standard ventricular catheter design featuring uniform inlet hole diameters and hole spacing has a standard deviation of 14.27% for the inlet flow rates, the optimized design has a standard deviation of 0.30%. CONCLUSIONS This customizable framework, paired with high-performance computing, provides a rapid method of design testing to solve complex flow problems. While a relatively simplified ventricular catheter model was used to demonstrate the framework, the computational approach is applicable to any baseline catheter model, and it is easily adapted to optimize catheters for the unique needs of different patients as well as for other fluid-based medical devices.
Neurosciences & Neurology↗