Simulation System Size Research Articles

A brief history of amyloid aggregation simulations

AbstractAmyloid proteins are characterized by their tendency to aggregate into amyloid fibrils, which are often associated with devastating diseases. Aggregation pathways typically involve unfolding or misfolding of monomeric proteins and formation of transient oligomers and protofibrils before the final aggregation product is formed. The conformational dynamics and polymorphic and volatile nature of these aggregation intermediates make their characterization by experimental techniques alone insufficient and also require computational approaches. Over the past 25 years, the size of simulated amyloid aggregation systems and the length of these simulations have increased significantly. These advances are discussed here. The review includes simulation approaches that model the aggregating peptides or proteins at both the all‐atom and coarse‐grained levels, use molecular dynamics simulations or Monte Carlo sampling to simulate the conformational changes, and present results for various amyloid peptides and proteins ranging from Lys‐Phe‐Phe‐Glu (KFFE) as the smallest system to as an intermediate‐sized peptide to α‐synuclein. The presentation of the history of amyloid aggregation simulations concludes with a discussion of where the future of these simulations may lie.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

Amorphous Zirconia-doped Tantala modeling and simulations using explicit multi-element spectral neighbor analysis machine learning potentials (EME-SNAP)

We model amorphous Zirconia-doped Tantala with machine learning interactomc potentials based on explicit multielement spectral neighbor analysis (EME-SNAP). These atomic structure models can reproduce partial radial distribution functions obtained from first-principles calculations and elastic moduli found from experimental measurements. The two-body pair forces calculated from EME-SNAP further affirm that the potentials capture the atomic interactions well. Molecular dynamics simulations of simulated annealing with EME-SNAP show that the final density of the amorphous models depends on the thermal history even when the annealing rate is kept constant, which captures experimental observations of history-dependent densities. Mechanical spectroscopy is also simulated using both Morse-Beest-Kramer-Santen pair potentials and EME-SNAP. The success in applying the EME-SNAP to amorphous Zirconia-doped Tantala pushes the boundaries of simulation accuracy and system size and enables better and more realistic atomistic modeling for amorphous systems. There are still some limitations in applying the potentials generated in this paper. They are only optimized for trained amorphous phases; high-temperature stability and transferability need to be further investigated.

New findings related to carbothermal reduction of polysiloxane-derived ceramics

Carbothermal reduction is the primary mechanism for SiC formation in polymer derived ceramics. However, the specific process involved in the SiOC conversion to SiC and the role of oxygen and carbon are unknown. Using ReaxFF with LAMMPS in a large simulation system containing more than 93 K atoms, this study for the first time shows that O must be removed from SiO2 clusters for SiC formation, providing insights into the carbothermal reduction mechanism. SiO2 regions need to be of a large enough size to induce O reduction. C cluster growth is inversely related to removal of O. This work shows not only the importance of the simulation system size in understanding atomic interactions but also absence of phase separation of SiOC to SiO2. Instead, SiO2 converts to SiC through C diffusion and O removal.

Uncovering the Mechanism of Size Effect on the Thermomechanical Properties of Highly Cross-Linked Epoxy Resins.

Epoxy resins are widely used as matrix resins, especially for carbon-fiber-reinforced plastic, due to their outstanding physical and mechanical properties. To date, most research into cross-linking processes using simulation has considered only a distance-based criterion to judge the probability of reaction. In this work, a new algorithm was developed for use with the large-scale atomic/molecular massively parallel simulator (LAMMPS) simulation package to study the cross-linking process; this new approach combines both a distance-based criterion and several kinetic criteria to identify whether the reaction has occurred. Using this simulation framework, we investigated the effect of model size on predicted thermomechanical properties of three different structural systems: diglycidyl ether of bisphenol A (DGEBA)/4,4'-diaminodiphenyl sulfone (4,4'-DDS), DGEBA/diethylenetriamine (DETA), and tetraglycidyl diaminodiphenylmethane (TGDDM)/4,4'-DDS. Derived values of gel point, volume shrinkage, and cross-linked resin density were found to be insensitive to model size in these three systems. Other thermomechanical properties, i.e., glass-transition temperature, Young's modulus, and yield stress, were found to reach stable values for systems larger than ∼40 000 atoms for both DGEBA/4,4'-DDS and DGEBA/DETA. However, these same properties modeled for TGDDM/4,4'-DDS did not stabilize until the system size reached 50 000 atoms. Our results provide general guidelines for simulation system size and procedures to more accurately predict the thermomechanical properties of epoxy resins.

Superscalability of the random batch Ewald method.

Coulomb interaction, following an inverse-square force-law, quantifies the amount of force between two stationary and electrically charged particles. The long-range nature of Coulomb interactions poses a major challenge to molecular dynamics simulations, which are major tools for problems at the nano-/micro-scale. Various algorithms are developed to calculate the pairwise Coulomb interactions to a linear scale, but poor scalability limits the size of simulated systems. Here, we use an efficient molecular dynamics algorithm with the random batch Ewald method on all-atom systems where the complete Fourier components in the Coulomb interaction are replaced by randomly selected mini-batches. By simulating the N-body systems up to 108 particles using 10 000 central processing unit cores, we show that this algorithm furnishes O(N) complexity, almost perfect scalability, and an order of magnitude faster computational speed when compared to the existing state-of-the-art algorithms. Further examinations of our algorithm on distinct systems, including pure water, a micro-phase separated electrolyte, and a protein solution, demonstrate that the spatiotemporal information on all time and length scales investigated and thermodynamic quantities derived from our algorithm are in perfect agreement with those obtained from the existing algorithms. Therefore, our algorithm provides a promising solution on scalability of computing the Coulomb interaction. It is particularly useful and cost-effective to simulate ultra-large systems, which is either impossible or very costly to conduct using existing algorithms, and thus will be beneficial to a broad range of problems at nano-/micro-scales.

Finite size effect on the existence of the liquid–vapour spinodal curve

Fluids interacting with Lennard-Jones and Square Well potentials reach thermodynamic equilibrium forming two coexisting phases when simulated within the liquid–vapour spinodal region. If molecular dynamics simulations in the NVT ensemble are performed on a fluid kept in a cubic cell with periodic boundary conditions, liquid and vapour phases at equilibrium are separated by a process of spinodal decomposition. In the vapour side of the spinodal curve, spherical, cylindrical, and slab type liquid structures are formed, depending on the total density. Under the mentioned conditions, in the liquid side of the spinodal, vapour bubbles with the same shapes, surrounded by liquid, are formed. Results from this work suggest that as the simulation system size increases, the spinodal curve approaches the orthobaric, but it converges to a position closer to the coexistence curve but separated from it. The same thing happens for both potential models simulated. This is in sharp contrast with previous findings by Errington, McDowell, Binder and coworkers, where they conclude that ‘Only for infinitely large systems does the effective spinodal density converge to the macroscopic coexistence vapor density.’

Learning from Physics Experiments with Quantum Computers: Applications in Muon Spectroscopy

Computational physics is an important tool for analysing, verifying, and -- at times -- replacing physical experiments. Nevertheless, simulating quantum systems and analysing quantum data has so far resisted an efficient classical treatment in full generality. While programmable quantum systems have been developed to address this challenge, the resources required for classically intractable problems still lie beyond our reach. In this work, we consider a new target for quantum simulation algorithms; analysing the data arising from physics experiments -- specifically, muon spectroscopy experiments. These experiments can be used to probe the quantum interactions present in condensed matter systems. However, fully analysing their results can require classical computational resources scaling exponentially with the simulated system size, which can limit our understanding of the studied system. We show that this task may be a natural fit for the coming generations of quantum computers. We use classical emulations of our quantum algorithm on systems of up to 29 qubits to analyse real experimental data, and to estimate both the near-term and error corrected resources required for our proposal. We find that our algorithm exhibits good noise resilience, stemming from our desire to extract global parameters from a fitted curve, rather than targeting any individual data point. In some respects, our resource estimates go further than some prior work in quantum simulation, by estimating the resources required to solve a complete task, rather than just to run a given circuit. Taking the overhead of observable measurement and calculating multiple datapoints into account, we find that significant challenges still remain if our algorithm is to become practical for analysing muon spectroscopy data.

Electronic transport in molecular junctions: The generalized Kadanoff-Baym ansatz with initial contact and correlations.

The generalized Kadanoff-Baym ansatz (GKBA) offers a computationally inexpensive approach to simulate out-of-equilibrium quantum systems within the framework of nonequilibrium Green's functions. For finite systems, the limitation of neglecting initial correlations in the conventional GKBA approach has recently been overcome [Karlsson et al., Phys. Rev. B 98, 115148 (2018)]. However, in the context of quantum transport, the contacted nature of the initial state, i.e., a junction connected to bulk leads, requires a further extension of the GKBA approach. In this work, we lay down a GKBA scheme that includes initial correlations in a partition-free setting. In practice, this means that the equilibration of the initially correlated and contacted molecular junction can be separated from the real-time evolution. The information about the contacted initial state is included in the out-of-equilibrium calculation via explicit evaluation of the memory integral for the embedding self-energy, which can be performed without affecting the computational scaling with the simulation time and system size. We demonstrate the developed method in carbon-based molecular junctions, where we study the role of electron correlations in transient current signatures.

The critical model size for simulating the structure-dynamics correlation in bulk metallic glasses

Computational simulation provides an effective way of understanding the disordered structure and structure-property relationships for metallic glass systems. Here, we systematically investigated the finite-size effects of the static structure and dynamical behaviors in a three-dimensional Cu50Zr50 model metallic glass via classical molecular dynamics (MD) simulations. It was found that the local structure is insensitive to the system size while the dynamical properties present evident finite-size effects. The decoupling between local structure and relaxation dynamics in the investigated supercooling emerges when the system contains less than ~2000 atoms. However, the collapse can be observed between the structural relaxation time and the dynamical heterogeneity for different sized systems across the whole range of our investigation. Our results support the intrinsic link between the structural relaxation time and dynamic heterogeneity and reveal the critical simulated system size for representing the structural origins of dynamics in bulk metallic glass with ignorable surface effects.

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

THE ONSET OF JET QUENCHING PHENOMENON

The aim of this study is to set a baseline for the jet quenching measurements of the Quark Gluon Plasma (QGP) formed in the large system size Nucleus-Nucleus (A-A) at top central collisions, via studying simulated small system size, Nucleon-Nucleon (N-N) collisions. The proton-proton (p-p) collisions were simulated using PYTHIA, at center of mass energies $\sqrt{s_{_{NN}}} = 200$ $GeV$ and $\sqrt{s_{_{NN}}} = 13$ $TeV$ corresponding to the available energies at the current collider experiments; the Relativistic Heavy Ion Collider (RHIC), and the Large Hadron Collider (LHC). At both energies, the two-particle azimuthal correlation functions have been considered, and the yield associated with the high transverse momentum ($p_{_{T}}$) particles were extracted at its near-side ($\Delta\phi \approx 0$) and away-side ($\Delta\phi \approx \pi$) at mid pseudo rapidity ($|\eta| \le 2$). The ratio between the near-side yields in the high multiplicity events to these of the low multiplicity events ($I_{_{HL}}^{^{N}}$), as well as, the ratio of the away-side yields ($I_{_{HL}}^{^{A}}$) were calculated at both energies as a function of the hadron fractional energy $z_{_{T}}$ of the high-$p_{_{T}}$ particle. At both energies, the values of $I_{_{HL}}^{^{N}}$ and $I_{_{HL}}^{^{A}}$ were less than unity, and of trivial dependence on $z_{_{T}}$. The values of $I_{_{HL}}^{^{A}}$ are always less than these of $I_{_{HL}}^{^{N}}$ at the same multiplicity and energy, and both quantities show a pattern of systematic decreases with the multiplicity. Such multiplicity dependence cannot be used neither to exclude the jet quenching nor to prove it in the high multiplicity events in p-p collisions, as the suppressions have been found at both sides, near and away of the high-$p_{_{T}}$ particle.

Onset of the Jet Quenching Phenomenon

The aim of this study is to set a baseline for the jet quenching measurements of the Quark Gluon Plasma (QGP) formed in the large system size Nucleus-Nucleus (A-A) at top central collisions, via studying simulated small system size, Nucleon-Nucleon (N-N) collisions. The proton-proton (p-p) collisions were simulated using PYTHIA, at center of mass energies $\sqrt{s_{_{NN}}} = 200$ $GeV$ and $\sqrt{s_{_{NN}}} = 13$ $TeV$ corresponding to the available energies at the current collider experiments; the Relativistic Heavy Ion Collider (RHIC), and the Large Hadron Collider (LHC). At both energies, the two-particle azimuthal correlation functions have been considered, and the yield associated with the high transverse momentum ($p_{_{T}}$) particles were extracted at its near-side ($\Delta\phi \approx 0$) and away-side ($\Delta\phi \approx \pi$) at mid pseudo rapidity ($|\eta| \le 2$). The ratio between the near-side yields in the high multiplicity events to these of the low multiplicity events ($I_{_{HL}}^{^{N}}$), as well as, the ratio of the away-side yields ($I_{_{HL}}^{^{A}}$) were calculated at both energies as a function of the hadron fractional energy $z_{_{T}}$ of the high-$p_{_{T}}$ particle. At both energies, the values of $I_{_{HL}}^{^{N}}$ and $I_{_{HL}}^{^{A}}$ were less than unity, and of trivial dependence on $z_{_{T}}$. The values of $I_{_{HL}}^{^{A}}$ are always less than these of $I_{_{HL}}^{^{N}}$ at the same multiplicity and energy, and both quantities show a pattern of systematic decreases with the multiplicity. Such multiplicity dependence cannot be used neither to exclude the jet quenching nor to prove it in the high multiplicity events in p-p collisions, as the suppressions have been found at both sides, near and away of the high-$p_{_{T}}$ particle.

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

An innovative magnetic state generator using machine learning techniques

We propose a new efficient algorithm to simulate magnetic structures numerically. It contains a generative model using a complex-valued neural network to generate k-space information. The output information is hermitized and transformed into real-space spin configurations through an inverse fast Fourier transform. The Adam version of stochastic gradient descent is used to minimize the magnetic energy, which is the cost of our algorithm. The algorithm provides the proper ground spin configurations with outstanding performance. In model cases, the algorithm was successfully applied to solve the spin configurations of magnetic chiral structures. The results also showed that a magnetic long-range order could be obtained regardless of the total simulation system size.

PCAViz: An Open-Source Python/JavaScript Toolkit for Visualizing Molecular Dynamics Simulations in the Web Browser.

Molecular dynamics (MD) simulations reveal molecular motions at atomic resolution. Recent advances in high-performance computing now enable microsecond-long simulations capable of sampling a wide range of biologically relevant events. But the disk space required to store an MD trajectory increases with simulation length and system size, complicating collaborative sharing and visualization. To overcome these limitations, we created PCAViz, an open-source toolkit for sharing and visualizing MD trajectories via the web browser. PCAViz includes two components: the PCAViz Compressor, which compresses and saves simulation data; and the PCAViz Interpreter, which decompresses the data in users’ browsers and feeds it to any of several browser-based molecular-visualization libraries (e.g., 3Dmol.js, NGL Viewer, etc.). An easy-to-install WordPress plugin enables “plug-and-play” trajectory visualization. PCAViz will appeal to a broad audience of researchers and educators. The source code is available at http://durrantlab.com/pcaviz/, and the WordPress plugin is available via the official WordPress Plugin Directory.

Effects of system size and cooling rate on the structure and properties of sodium borosilicate glasses from molecular dynamics simulations.

Borosilicate glasses form an important glass forming system in both glass science and technologies. The structure and property changes of borosilicate glasses as a function of thermal history in terms of cooling rate during glass formation and simulation system sizes used in classical molecular dynamics (MD) simulation were investigated with recently developed composition dependent partial charge potentials. Short and medium range structural features such as boron coordination, Si and B Qn distributions, and ring size distributions were analyzed to elucidate the effects of cooling rate and simulation system size on these structure features and selected glass properties such as glass transition temperature, vibration density of states, and mechanical properties. Neutron structure factors, neutron broadened pair distribution functions, and vibrational density of states were calculated and compared with results from experiments as well as ab initio calculations to validate the structure models. The results clearly indicate that both cooling rate and system size play an important role on the structures of these glasses, mainly by affecting the 3B and 4B distributions and consequently properties of the glasses. It was also found that different structure features and properties converge at different sizes or cooling rates; thus convergence tests are needed in simulations of the borosilicate glasses depending on the targeted properties. The results also shed light on the complex thermal history dependence on structure and properties in borosilicate glasses and the protocols in MD simulations of these and other glass materials.

System-Size Dependence of Electrolyte Activity Coefficients in Molecular Simulations.

A systematic study of the dependence of electrolyte activity coefficients on simulation system size has been undertaken. Using implicit-solvent simulations for which calculations with low statistical uncertainty are feasible, it was found that the chemical potential for a NaCl model depends strongly on simulation system size at concentrations up to about 0.3 mol/L; system-size effects at higher concentrations are much smaller. Similar trends were confirmed in systems with an explicit solvent. System-size effects on the chemical potential, when uncorrected, can lead to systematic errors in the activity coefficient greater than 10%. The rigorous method to correct for such system-size effects is to perform multiple simulations at each concentration and extrapolate to infinite system size. Unfortunately, this becomes impractical for explicit-solvent simulations at low concentrations, because of computational limitations that lead to large statistical uncertainties in the results. Somewhat counterintuitively, we find that lower systematic errors for the Henry's law reference chemical potential are obtained by using simulations at higher concentrations, for which system-size effects are much smaller, to obtain estimates for the reference chemical potential. This is the case even though at these higher concentrations deviations from the Debye-Hückel limiting law (or its empirical extensions) are greater than those at lower concentrations.

A derivation and scalable implementation of the synchronous parallel kinetic Monte Carlo method for simulating long-time dynamics

Kinetic Monte Carlo (KMC) simulations are used to study long-time dynamics of a wide variety of systems. Unfortunately, the conventional KMC algorithm is not scalable to larger systems, since its time scale is inversely proportional to the simulated system size. A promising approach to resolving this issue is the synchronous parallel KMC (SPKMC) algorithm, which makes the time scale size-independent. This paper introduces a formal derivation of the SPKMC algorithm based on local transition-state and time-dependent Hartree approximations, as well as its scalable parallel implementation based on a dual linked-list cell method. The resulting algorithm has achieved a weak-scaling parallel efficiency of 0.935 on 1024 Intel Xeon processors for simulating biological electron transfer dynamics in a 4.2 billion-heme system, as well as decent strong-scaling parallel efficiency. The parallel code has been used to simulate a lattice of cytochrome complexes on a bacterial-membrane nanowire, and it is broadly applicable to other problems such as computational synthesis of new materials.

Accelerating Cell Dynamics Simulations of Soft Materials using CUDA-GPU

Soft materials are a highly demanded class of research for predicting the characteristics of phase separation and self-assembly into nanoscale structures. One of the most well-known methods to demonstrate and simulate dynamic behavior of particles, such as particle tracking, and to consider different effects of simulation parameters is cell dynamic simulation. In fact, cell dynamic simulation as a cellular computerization can be used to investigate different aspects of morphological topographies of soft material system. To achieve accurate and efficient computational results for cell dynamic simulation method, two factors are essential and vital: first, computing/calculating time-scale; and second, simulation system size. Consequently, finding available computing algorithms and resources such as sequential algorithm for implementing a complex technique and achieving precise results is critical and expensive. Therefore, it is very important to consider a parallel algorithm and programming model to solve time-consuming and massive computational processing. This paper presents a novel GPU-based parallel acceleration approach to cell dynamics simulations (CDS) for a spherical phase diblock copolymer under a shear flow. A new achievement is based on a commodity NVIDIA Quadro K5000 graphic card and Compute Unified Device Architecture (CUDA) C language.

Optimal sizing of residential PV-systems from a household and social cost perspective: A case study in Austria

In this paper we analyse optimal sizing of grid connected rooftop photovoltaic systems from a household’s perspective. We estimate the profit maximizing size for more than 800 households in Austria for various electricity tariffs and subsidy schemes considering economies of scale related to the investment costs of photovoltaic systems in the size range of 1–20kW of installed capacity. Size dependent investment costs are estimated from data on photovoltaic systems installed in Austria from 2008 to 2013. We then take a social cost perspective and relate the results to the total investment costs to install a certain amount of capacity in residential areas. We find that in the presence of economies of scale substantial cost inefficiencies can occur resulting from incentives to install relatively small systems. Depending on the compensation scheme the simulated optimal system size can be as low as 2kW resulting in high costs per capacity. Subsidy design and tariff regulations can be adopted to incentivize larger photovoltaic systems in the residential sector which would reduce the costs of achieving a certain level of distributed PV generation. It is estimated that for a minimum system size of 5kW total investment costs for subsidised residential photovoltaic systems in Austria from 2008 to 2013 could have been 2.2% lower for the same amount of installed capacity. We further argue that the strict focus on onsite use of electricity from photovoltaic systems in the residential area is not necessarily desirable from a social cost perspective because it can lead to small and therefore more expensive photovoltaic systems.