Engineering topics

Fiorin, Giacomo

Publications and source records attributed to Fiorin, Giacomo.

5 records

DOE OSTI2021

Bringing discrete-time Langevin splitting methods into agreement with thermodynamics

In light of the recently published complete set of statistically correct Grønbech–Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Finally, numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Molecular simulation data for 'Data-guided Multi-Map variables for ensemble refinement of molecular movies'

These trajectories, scripts, and analysis performed on Summit underly the work published as 'Data-guided Multi-Map variables for ensemble refinement of molecular movies'. The trajectories include equilibrium and non-equilibrium sampling of ADK, CODH, and FLPP3, the scripts used to build the systems, and the scripts used to analyze the output. The directory structure is explained further in an internal README file.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. In this study, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

high-performance computing↗

DOE OSTI2020

Different bonding type along each crystallographic axis: Computational study of poly($\textit{p}$-phenylene terephthalamide)

Poly($\textit{p}$-phenylene terephthalamide) (PPTA) exhibits van der Waals (vdW) bonding along its $\textit{a}$ axis, hydrogen bonding along its $\textit{b}$ axis, and covalent bonding along its $\textit{c}$ axis. In this work, we explore the structural and mechanical properties of PPTA using density functional theory with various functionals including LDA, PBE, PBE+rVV10L, SCAN, and SCAN+rVV10, compared with available experiments. The hierarchy of nonempirical semilocal functionals LDA, PBE, and SCAN (not fitted to any multicenter bonded system) includes differing amounts of intermediate-range vdW interaction. rVV10 is the long-range vdW correction. (rVV10L differs from rVV10 only in the value of a range parameter.) Among the tested functionals, SCAN shows the best performance for the lattice parameters of PPTA along the two crystal directions involving vdW or hydrogen-bond interaction. The equilibrium lattice constants obtained by SCAN and PBE+rVV10L are closest to experimental data, while SCAN+rVV10 slightly overbinds the system. We study the mechanical response of PPTA by applying strain along three lattice directions. Due to the inclusion of vdW interaction, SCAN, PBE+rVV10L, and SCAN+rVV10 all exhibit correct bonding strain-energy curves. On the contrary, PBE strongly underestimates the vdW interaction needed to resist uniaxial stretching along the $\textit{a}$ axis. The Young's modulus and yield strength of PPTA are computed and compared with previous results. The experimental values are much smaller than the computed ones, mainly due to the fact that the PPTA fiber samples used for measurements are mechanically weaker than the perfect molecular crystals considered in the simulations. Interestingly, when a compressive uniaxial stress of 25 Kbar is applied along the $\textit{b}$ axis, a structural phase transition, in which the hydrogen bonds reform along one diagonal of the $\textit{ab}$ rectangle, is predicted by SCAN.

36 MATERIALS SCIENCE↗