DOCSLIB.ORG

Supplementary Information

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Information Supplementary Information Structural ordering in liquid gallium under extreme conditions James W. E. Drewitt,1 Francesco Turci,2 Benedict J. Heinen,1 Simon G. Macleod,3, 4 Fei Qin,1 Annette K. Kleppe,5 and Oliver T. Lord1 1School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Bristol, BS8 1RJ, United Kingdom 2H H Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, United Kingdom 3Atomic Weapons Establishment, Aldermaston, Reading RG7 4PR, United Kingdom 4SUPA, School of Physics and Astronomy, and Centre for Science at Extreme Conditions, The University of Edinburgh, Mayfield Road, Edinburgh EH9 3JZ, United Kingdom 5Diamond Light Source Ltd, Diamond House, Harwell Science and Innovation Campus, Chilton, OX11 0DE, United Kingdom EXPERIMENTAL Sodium Chloride pressure calibrant In order to avoid deleterious alloying between Ga and Re, an NaCl annulus was placed in the pressure chamber to separate the sample from the Re gasket. The advantage of NaCl is that it has an equation of state that has been carefully calibrated as a function of p and T throughout the range of this study [1], it is highly compressible, leading to a high precision on the pressure determination, and it has a simple cubic structure, making its unit cell volume easy to determine from XRD data. The NaCl was stored in an oven at 130 ◦C prior to use to minimise its inital water content. At the start of each run, the cell was placed unsealed in the vacuum vessel which was evacuated before any pressure was applied, to help drive off any remaining moisture from the salt and the Ga sample. NaCl and KCl salts are the most common pressure media and calibrants used in high-p-T studies of metals in the DAC [2{4] and that when prepared carefully, as here, reactions are not observed during in situ XRD or ex situ chemical mapping. Iterative structure factor and number density refinement Our code LiquidDiffract [5] implements an iterative procedure first proposed by Eggert et al. [6] to provide the normalised SGaGa(Q) and gGaGa(r) functions, whilst simultaneously deriving a converged solution for the atomic number density, n0. This method optimises the Krogh-Moe-Norman [7, 8] normalization of X-ray intensities by exploiting the simple behavior of the reduced pair distribution function G(r) = 4πn0r[g(r) − 1] prior to the first interatomic distance G(r < rmin) = −4πn0r. As no atom-atom correlations are expected in this region, any oscillations are a result of e.g. systematic errors in the nomalization factor and the finite experimental Qmax < 1. LiquidDiffract computes the Fourier transform of the difference ∆Gi(r) = Gi(r) − (−4πn0r); (1) for r < rmin, and uses this to re-scale the reciprocal-space data for i iterations (a minimum of 3 iterations is normally required for convergence). A fortuitous consequence of this iterative auto-normalization procedure is that the bulk liquid number density can be determined directly by minimising a χ2 figure of merit Z rmin 2 2 χi (n0) = [∆Gi(r) ]dr (2) 0 which is known to have a well defined minimum [5, 6]. 2 DENSITY RELAXATION IN AB INITIO SIMULATIONS In order to measure the degree of decorrelation of density fluctuations in the ab initio molecular dynamics simulations we evaluate the self part of the intermediate scattering function at a fixed wavevector close to the first peak of the 1 radial distribution function q = 2π=2:8 A˚− . This is defined for a system of N particles as * N + 1 X iq (rn(t) rn(0)) Fs(q; t) = N − e− · − ; (3) n=1 which is averaged isotropically in order to remove the angular dependence. As shown in Fig. S1 for most state points (excepting the T = 303 K and the highest pressure state point at T = 400 K) the correlation functions decay to zero in a few picoseconds, ensuring equilibration. T =1000K T =800K T =600K T =400K T =303K 1.00 1.00 1.00 1.00 1.00 ) ) ) ) ) p[GPa] ,t ,t ,t ,t ,t 1 1 1 1 1 0 ° 0.75 p[GPa] ° 0.75 ° 0.75 ° 0.75 ° 0.75 ˚ A ˚ A ˚ A ˚ A ˚ A 8 8 8 8 8 . 3.2 13 . p[GPa] . 2 2 2 2 2 / 0.50 / 0.50 / 0.50 / 0.50 / 0.50 º 4.7 16 º 2.4 9.5 º p[GPa] º º 6.3 19 3.8 12 1.5 6.3 p[GPa] =2 0.25 =2 0.25 =2 0.25 =2 =2 k k k k 0.25 k 0.25 ( 8.2 23 ( 5.5 15 ( 2.9 8.4 ( 0.49 3.4 ( s s s s s F 10 33 F 7.3 18 F 4.5 11 F 1.9 F 0.00 0.00 0.00 0.00 0.00 101 102 103 101 102 103 101 102 103 101 102 103 101 102 103 t [fs] t [fs] t [fs] t [fs] t [fs] FIG. S1. Self part of the intermediate scattering function computed from the ab initio trajectories. HARD-SPHERE MAPPING Percus-Yevick approximation In this work, we compare the multi-body local structure in the ab initio simulations of gallium to the local structure of the hard sphere fluid. To do so, we employ the Percus-Yevick (PY) approximation to estimate the radial distribution function g(r) of hard spheres at volume fraction η and diameter σeff . For every ab initio dataset we find the optimal couple fη; σeff g that minimises the difference Z umax ab initio PY 2 δ(η; σeff ) = [(g (uσeff ) − 1)u − (gη (u) − 1)u] du; (4) umin where the integral is over a fixed interval umin = 1; umax = 4, where we implicitly assumed the hard spheres to have diameter one so that u is a scaled radial distance variable, with uσeff measured in angstroms. An example of the resulting fitting is reported in Fig. S2. By construction, the contact position of the hard spheres does not coincide with the first peak of the radial distribution function of gallium. The Percus-Yevick approximation is a closure to the Ornstein-Zernicke equation for hard spheres which is reasonably accurate for volume fractions below freezing ηfreezing ≈ 0:49 [9]. In particular, it provides an expression for the direct correlation function c(r) as ( c + c u + c u3 u < 1 c(u) = 0 1 3 (5) 0 otherwise 3 where 2 4 c0 = −(1 + 2η) =(1 − η) (6) 2 4 c1 = 6η(1 + η=2) =(1 − η) (7) c3 = c0η=2 (8) R 1 The radial Fourier transform isc ^(k) = 4π 0 duc(u) sin(ku)u=k and can be input into the Ornstein-Zernicke equation p[GP a] 2.4 7.3 14.9 10 3.8 9.5 18.3 5.5 12.0 8 ) 6 r ( g 4 2 0 0 1 2 3 4 r/σeff FIG. S2. Percus-Yevick fits (dashed lines) to the radial distribution functions of gallium from ab initio simulations at temper- ature T = 800K and varying pressure (continuous lines). The lines are shifted to ease the visualisation. h^(k) =c ^(k)=(1 − 6ηc^(k)/π); (9) 1 R so that h(u) = g(u) − 1 can be obtained via an inverse radial Fourier transform as h(u) = 1 dkh^(k) sin(ku)k=u. 2π2 0 We numerically compute the radial distribution function following the PY route and minimise δ(η; σeff ) in order to find the optimal volume fraction (and hard sphere diameter) that best approximates the gallium radial distribution function. Event-driven molecular dynamics From the fitting to the Percus-Yevick radial distribution function we obtain a range of diameters and volume fractions for the mapped hard-sphere system. In Fig. S3 we see that while the effective hard-sphere diameter is bracketed in the range [2:4; 2:7]A,˚ the volume fraction has a more pronounced systematic variation towards high volume fractions with increasing the pressure at a given temperature, as expected. In order to measure the multi-body 4 (a)1000 (b) 1000 2.60 0.44 800 800 2.55 ] ] 0.42 K K ¥ eff [ [ æ T 600 2.50 T 600 0.40 2.45 400 400 0.38 2.40 0 20 0 20 p[GP a] p[GP a] FIG. S3. Variation of the Percus-Yevick fitting parameters σeff and volume fraction η across the p − T phase diagram. correlations in the hard-sphere system { as described in the main text { we run constant-volume event-driven molecular dynamics simulations employing the highly optimised DynamO package [10] to obtain equilibrated configurations of N = 600 particles (we select this number to match the number of particles employed in the ab initio runs). To ensure equilibration, the simulations are run for at least 100 structural relaxation times, which is the time for decorrelation of density fluctuations. REVERSE MONTE-CARLO COMPARISON Reverse Monte-Carlo (RMC) results are obtained using fullrmc 4.0.0, a Python package with specific optimisa- tions inspired by machine-learning algorithms [11]. These methods involve the generation of three-dimensional point clouds whose spatial distribution is optimal according to some metrics. Typically, RMC optimizations minimise the difference between some structural observable measured by other means (e.g. from experiments) and the generated point cloud. In principle, RMC simulations are not bound to a particular observable to optimise: constraints on radial distributions can be complemented with information on the angular distributions or eventually even more complex best-fit functions. Here we employ RMC in a naive way, in line with what is oftentimes reported in the literature for the structure of liquids and glasses.
Load more

Recommended publications
  • University of Cincinnati
    UNIVERSITY OF CINCINNATI Date: ___________________8th August 2008 I, ________________________________________________Prashant Rajan _________, hereby submit this work as part of the requirements for the degree of: Master of Science in: Department of Chemical and Materials Engineering It is entitled : Understanding aggregate morphology in colloidal systems through small-angle scattering and reverse Monte Carlo (RMC) simulations This work and its defense approved by: Chair: _______________________________Dale W. Schaefer _______________________________Greg Beaucage _______________________________Jude Iroh _______________________________Steve Clarson _______________________________ Understanding aggregate morphology in colloidal systems through small- angle scattering and reverse Monte Carlo simulations A thesis submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Science in the Department of Chemical and Materials Engineering of the College of Engineering 2008 by Prashant Rajan B.E. University of Pune, Pune, India Committee Chair: Professor Dale W. Schaefer Abstract We have studied the three dimensional structure of aggregated colloidal silica at sub- micron length scales. Our efforts are part of an integrated research project[1-3] focused on constructing a verifiable model for reinforcement of elastomers by colloidal fillers using small- angle scattering techniques. Our work is aimed at developing a simulation-based modeling tool that enables us to visualize the microstructure of reinforcing fillers in three dimensions by fitting small-angle scattering data. We have developed a program based on ‘ C’ programming language to describe scattering from a single aggregate. Our simulation creates the aggregate using a particle-cluster aggregation mechanism. The simulation then proceeds to make random moves on the aggregate surface while simultaneously fitting small angle scattering data on the system under study.
    [Show full text]
  • Dr Matt Tucker
    Dr Matt Tucker Diffraction Group Leader, Neutron Sciences Directorate, One Bethel Valley Road, MS-6475, Oak Ridge, TN, 37831 Mobile: +1 865 340 5950 E-Mail: [email protected] Current Employer: UT-Battelle Employment and education Diffraction Group Leader Oak Ridge National Lab, USA 2017 – Present Advance Diffraction Group Leader Spallation Neutron Source, USA 2016 – 2017 Joint appointment with Diamond Light Source Ltd, UK 2013 – 2016 Instrument Scientist on Polaris at ISIS spallation source, UK 2013 – 2016 Instrument Scientist on PEARL at ISIS spallation source, UK 2005 – 2013 Postdoctoral Researcher, Earth Sciences, University of Cambridge, UK 1998 – 2005 PhD in Condensed Matter Physics, University of Kent at Canterbury, UK 1995 – 1998 BSc (Hons) Physics with Medical Physics, Cardiff University, UK 1992 – 1995 General Skills Scientific: Powder diffraction, Total scattering, High pressure studies, Chemical synthesis, Rietveld analysis, McStas simulations and Reverse Monte Carlo analysis and software development. Computing: Languages used: Fortran, Python, C, C++, C shell; Operating systems: Windows, Linux, Mac OS, VMS. Communication: I give regular presentations at national and international meetings. I am the co-author of over 155 scientific publications. I have arranged and chaired several scientific meetings, conference sessions and PDF workshops. As described above, I have regular meetings with my group members and external teams to ensure clear and effective communication. International & National presentations (15 talks, 6 countries)
    [Show full text]
  • Modeling the Amorphous Structure of a Mechanically Alloyed
    Modeling the amorphous structure of a mechanically alloyed Ti50Ni25Cu25 alloy using anomalous wide-angle x-ray scattering and reverse Monte Carlo simulations J.C. de Limaa,*, C.M. Poffob, S.M. Souzac, K.D. Machadod, D.M. Trichêsc, T.A. Grandia, R.S. de Biasie aDepartamento de Física, Universidade Federal de Santa Catarina, Campus Universitário Trindade, S/N, C.P. 476, 88040-900 Florianópolis, Santa Catarina, Brazil bDepartamento de Engenharia Mecânica, Universidade Federal de Santa Catarina, Campus Universitário Trindade, S/N, C.P. 476, 88040-900 Florianópolis, Santa Catarina, Brazil cDepartamento de Física, Universidade Federal do Amazonas, 3000 Japiim, 69077-000 Manaus, Amazonas, Brazil. dDepartamento de Física, Centro Politécnico, Universidade Federal do Paraná, 81531- 990, Curitiba, Paraná, Brazil eSeção de Engenharia Mecânica e de Materiais, Instituto Militar de Engenharia, 22290-270 Rio de Janeiro, RJ, Brazil A B S T R A C T An amorphous Ti50Ni25Cu25 alloy was produced by 19 h of mechanical alloying. Anomalous wide angle x-ray scattering data were collected at six energies and six total scattering factors were obtained. By considering the data collected at two energies close to the Ni and Cu K edges, two differential anomalous scattering factors around the Ni and Cu atoms were obtained, showing the chemical environments around these atoms are different. The eight factors were used as input data to the reverse Monte Carlo method used to compute the partial structure factors STi-Ti(K), STi-Cu(K), STi-Ni(K), SCu- Cu(K), SCu-Ni(K) and SNi-Ni(K). From their Fourier transformation, the partial pair distribution functions GTi-Ti(r), GTi-Cu(r), GTi-Ni(r), GCu-Cu(r), GCu-Ni(r) and GNi-Ni(r) were obtained, and the coordination numbers and interatomic atomic distances for the first neighbors were determined.
    [Show full text]
  • Structure Modeling with X-Ray Absorption and Reverse Monte Carlo: Applications to Water
    Mikael Leetmaa Mikael Structure Modeling with X-ray Absorption and Reverse Monte Carlo: Applications to Water Mikael Leetmaa Reverse Monte Carlo: Applications to Water to Applications Monte Carlo: Reverse Absorption and Structure Modeling with X-ray ISBN 978-91-7155-972-2 Physics Department Doctoral Thesis in Chemical Physics at Stockholm University, Sweden 2009 Abstract Water is an important substance. It is part of us, of our environment, and is a fundamental prerequisite for the existence of life as we know it. The structure of water is still, after over 100 years of research on the subject, however under debate. In this thesis x-ray absorption spectroscopy (XAS) and reverse Monte Carlo (RMC) modeling are used to search for structural solutions of water consistent with many different experimental data sets, with emphasis on the combination of different ex- perimental techniques for a reliable structure determination. Neutron and x-ray diffraction are analyzed in combination with the more recent synchrotron radiation based XAS. Geometrical criteria for H-bonding are implemented in RMC to drive the fits and allow to evaluate differently H-bonded structure models against the data. It is shown that the available diffraction data put little constraints on the type of H-bond topology or O-O-O tetrahedrality for the structure of liquid water. It is also demonstrated that classical MD simulations, using some of the most common interaction potentials for water, give rise to O-O and O-H pair-correlation functions with too sharp first peaks at too short distances to be in agreement with diffraction, and furthermore that requiring a large fraction of broken H-bonds is not in itself enough for a structure model to reproduce the experimental XAS.
    [Show full text]
  • 2016 Neutron Experiment Descriptions
    2016 Neutron Experiment descriptions: N1: Triple-Axis Spectrometers, HFIR HB-1A & HB-3 Spin wave and phonon dispersion in Fe-Ga solid solutions Fe-Ga alloys with appropriate composition and heat treatment, exhibit giant magnetostriction in a polycrystalline and ductile form. The tetragonal magnetostriction coefficient, λ100, of Fe-Ga can be up to 15 times that of pure Fe. This makes these materials of tremendous scientific and technological interest for use in devices such as actuators, transducers and sensors. Elastic constant measurements show that the shear elastic constant 1/2(C11-C12) decreases with increasing gallium concentration and extrapolates to zero at approximately 26 at.% Ga. The slope of the phonon dispersion curve at low-q of the T2[110] branch is a measure of that elastic constant and hence the interest in measuring phonons in these materials. With the large magnetoelastic interactions in such a material, it is also of interest to measure the spin wave dispersion. The triple-axis spectrometers HB-1A and HB-3 will be used to measure both phonon and spin waves of two compositions of Fe-Ga alloys. N2: Powder Diffractometer, HFIR HB-2A Magnetic structure of NiO Neutron diffraction measurements will be performed to investigate the onset of long- range magnetic order in NiO. Data will be collected at various temperatures, ranging from 600K to 288K, using the Neutron Powder Diffractometer at the HFIR. Rietveld analysis of the crystal and low-temperature magnetic structure will be carried out using FullProf Suite software. The results obtained will be discussed and compared with those reported in earlier studies.
    [Show full text]
  • A Guide to Monte Carlo Simulations in Statistical Physics, Third Edition
    This page intentionally left blank A Guide to Monte Carlo Simulations in Statistical Physics Third Edition Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. This third edition contains extensive new material describing numerous powerful new algorithms that have appeared since the previous edition. It highlights recent technical advances and key applications that these algo- rithms now make possible. With several new sections and a new chapter on the use of Monte Carlo simulations of biological molecules, this edition expands the discussion of Monte Carlo at the periphery of physics and beyond. Throughout the book there are many applications, examples, recipes, case studies, and exercises to help the reader understand the material. It is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques which have become a third tool of physical science, complementing experiment and analytical theory. DAVID P. LANDAU is the Distinguished Research Professor of Physics and founding Director of the Center for Simulational Physics at the University of Georgia. KURT BINDER is Professor of Theoretical Physics and Gutenberg Fellow at the Institut fu¨r Physik, Johannes-Gutenberg-Universita¨t Mainz, Germany. AGuideto Monte Carlo Simulations in Statistical Physics Third Edition David P. Landau Center for Simulational Physics, The University of Georgia Kurt Binder Institut fu¨r Physik, Johannes-Gutenberg-Universita¨t Mainz CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Dubai, Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521768481 © D.
    [Show full text]
  • Rmcprofile User Manual
    RMCProfile User Manual Code version 6.7.4 Matt Tucker, Martin Dove, Andrew Goodwin, Anthony Phillips, David Keen, Helen Playford, Wojciech A. Slawinski, Igor Levin, Victor Krayzman, Maksim Eremenko, Yuanpeng Zhang February 21, 2019 RMCProfile Manual v6.7.4 CONTENTS Contents 1 Introduction 3 1.1 We know you don’t read manuals!3 1.2 What and why of RMC4 1.3 RMCProfile introduction6 2 Capabilities 9 2.1 Fitting neutron and X-ray total scattering data9 2.2 Fitting PDF data 10 2.3 Fitting the Bragg profile 11 2.4 Distance-window constraints 12 2.5 Interatomic potentials 13 2.6 Bond valence constraints 13 2.7 Modelling atomic site disorder through atom-swap moves 14 2.8 Coordination Constraints 14 2.9 Polyhedral restraints 14 2.10 Magnetic structure modelling 15 2.11 Fitting EXAFS data 23 2.12 Producing starting configurations 23 2.13 XML output files 24 3 Installing and Running RMCProfile 28 3.1 Installation 28 3.2 Setting up the files for an RMCProfile calculation 29 3.3 Running RMCProfile 30 3.4 Output files 31 4 Input Files 33 4.1 RMCProfile main data file 33 4.2 Neutron and X-ray coefficients 62 4.3 Using experimental data 63 4.4 Using magnetism 67 4.5 Using the Bragg profile 69 4.6 Using EXAFS data 70 4.7 Using potentials 70 4.8 Using Bond valence sum 80 4.9 Using constraints and restraints 81 4.10 Polyhedral restraints 81 4.11 RMC Version 6 format configuration files 87 4.12 Experimental data files 90 4.13 Bragg scattering 92 4.14 RMCProfile version 3 files 94 4.15 Upgrade to v6 98 1/176 RMCProfile Manual v6.7.4 CONTENTS 5 Examples 100 5.1 Using scattering
    [Show full text]
  • Inversion of Diffraction Data for Amorphous Materials
    The University of Southern Mississippi The Aquila Digital Community Faculty Publications 9-22-2016 Inversion of Diffraction Data for Amorphous Materials Anup Pandey Ohio University Parthapratim Biswas University of Southern Mississippi, [email protected] D.A. Drabold Ohio University Follow this and additional works at: https://aquila.usm.edu/fac_pubs Part of the Physics Commons Recommended Citation Pandey, A., Biswas, P., Drabold, D. (2016). Inversion of Diffraction Data for Amorphous Materials. Scientific Reports, 6, 1-8. Available at: https://aquila.usm.edu/fac_pubs/16741 This Article is brought to you for free and open access by The Aquila Digital Community. It has been accepted for inclusion in Faculty Publications by an authorized administrator of The Aquila Digital Community. For more information, please contact [email protected]. www.nature.com/scientificreports OPEN Inversion of diffraction data for amorphous materials Anup Pandey1, Parthapratim Biswas2 & D. A. Drabold3 The general and practical inversion of diffraction data–producing a computer model correctly Received: 06 July 2016 representing the material explored–is an important unsolved problem for disordered materials. Such Accepted: 31 August 2016 modeling should proceed by using our full knowledge base, both from experiment and theory. In this Published: 22 September 2016 paper, we describe a robust method to jointly exploit the power of ab initio atomistic simulation along with the information carried by diffraction data. The method is applied to two very different systems: amorphous silicon and two compositions of a solid electrolyte memory material silver-doped GeSe3. The technique is easy to implement, is faster and yields results much improved over conventional simulation methods for the materials explored.
    [Show full text]
  • Inverse Monte Carlo Methods
    CHAPTER 1 Inverse Monte Carlo Methods Alexander P. Lyubartsev CONTENTS 1.1 Introduction 1 1.2 Multiscale Simulations Using IMC 4 1.2.1 Theoretical Background 4 1.2.2 IMC: Newton Inversion 6 1.2.3 IMC: Reconstruction of Pair Potentials from RDFs 7 1.2.4 Soware 10 1.3 Applications of the IMC 11 1.3.1 Simple Electrolytes 11 1.3.2 CG Lipid Model 13 1.3.3 CG DNA Models 17 1.3.4 Other Systems 19 1.4 Final Remarks 21 Acknowledgments 22 References 22 1.1 INTRODUCTION Modeling of many important biomolecular and so matter systems requires consideration of length and time scales not reachable by atomistic simula- tions. An evident solution of this problem is introducing simplified models with lower spacial resolution, which have received a common name: coarse- grained (CG) models. In CG models, atoms of (macro)molecules are united into CG sites and solvent atoms are oen not considered explicitly. This reduces greatly the number of degrees of freedom of the studied system and allows simulations of much larger systems which are not feasible to 1 2 ◾ Coarse-Grained Modeling of Biomolecules simulate at the atomistic level. Studies of models which can be character- ized as “coarse-grained” started at the earlier stages of molecular modeling in the 1960s and 1970s (when the term “coarse-grained” was not used at all). For example, a primitive model of electrolytes represented hydrated ions as charged spheres in a dielectric media (Vorontsov-Velyaminov and Elyashe- vich, 1966; Card and Valleau, 1970), and a simple freely jointed model of a polymer chain (Gottlieb and Bird, 1976) was used to model polymers in solution.
    [Show full text]
  • Pdffit2 and Pdfgui: Computer Programs for Studying
    IOP PUBLISHING JOURNAL OF PHYSICS: CONDENSED MATTER J. Phys.: Condens. Matter 19 (2007) 335219 (7pp) doi:10.1088/0953-8984/19/33/335219 PDFfit2 and PDFgui: computer programs for studying nanostructure in crystals CLFarrow1, P Juhas1,JWLiu1, D Bryndin1,4,ESBoˇzin1,JBloch2, Th Proffen3 and S J L Billinge1 1 Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824-2320, USA 2 Institute for Theoretical Physics, University of Regensburg, 93040 Regensburg, Germany 3 Lujan Neutron Scattering Center, Los Alamos National Laboratory, Los Alamos, NM 87545, USA 4 Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA E-mail: [email protected] Received 29 March 2007 Published 4 July 2007 Online at stacks.iop.org/JPhysCM/19/335219 Abstract PDFfit2 is a program as well as a library for real-space refinement of crystal structures. It is capable of fitting a theoretical three-dimensional (3D) structure to atomic pair distribution function data and is ideal for nanoscale investigations. The fit system accounts for lattice constants, atomic positions and anisotropic atomic displacement parameters, correlated atomic motion, and experimental factors that may affect the data. The atomic positions and thermal coefficients can be constrained to follow the symmetry requirements of an arbitrary space group. The PDFfit2 engine is written in C++ and is accessible via Python, allowing it to inter-operate with other Python programs. PDFgui is a graphical interface built on the PDFfit2 engine. PDFgui organizes fits and simplifies many data analysis tasks, such as configuring and plotting multiple fits. PDFfit2 and PDFgui are freely available via the Internet.
    [Show full text]
  • Marshall Mcdonnell
    Marshall McDonnell Diffraction Group, Neutron Sciences Directorate Home Address Oak Ridge National Laboratory 7312 Martingale Drive PO Box 2008, Oak Ridge, TN 37831-6454 Powell, TN 37849 Phone: (865) 560-6227 Email: [email protected] SUMMARY Since April 2019 I have been a research software engineer in the Research Software Engineering group at Oak Ridge National Laboratory, USA. My research interests focus on developing software with sound engineering practices with a focus on applications in materials research. I am the co-author of 10 refereed publications with 8 manuscripts forthcoming (accepted, under review, submitted, or in preparation), co-author of 32 presentations (oral and poster), an active open-source software developer, and co-instructor for 4 workshops around the world. PROFESSIONAL EXPERIENCE 2019- Research Software Engineer Research Software Engineering Group Computer Science and Mathematics Division, Computing and Computational Sciences Directorate Oak Ridge National Laboratory, Oak Ridge, TN Supervisor: Jay Jay Billings 2016-2019 Postdoctoral Research Associate Diffraction Group Neutron Scattering Division, Neutron Sciences Directorate Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN Advisor: Matthew Tucker 2011-2016 Graduate Student Research Assistant Computational Materials Research Group Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN Advisor: David Keffer EDUCATION 2011-2016 Doctor of Philosophy, University of Tennessee Knoxville, TN Chemical and Biomolecular Engineering
    [Show full text]
  • Monte Carlo Simulation of Polymers: Coarse-Grained Models
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Juelich Shared Electronic Resources John von Neumann Institute for Computing Monte Carlo Simulation of Polymers: Coarse-Grained Models Jorg¨ Baschnagel, Joachim P. Wittmer, Hendrik Meyer published in Computational Soft Matter: From Synthetic Polymers to Proteins, Lecture Notes, Norbert Attig, Kurt Binder, Helmut Grubmuller¨ , Kurt Kremer (Eds.), John von Neumann Institute for Computing, Julich,¨ NIC Series, Vol. 23, ISBN 3-00-012641-4, pp. 83-140, 2004. c 2004 by John von Neumann Institute for Computing Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires prior specific permission by the publisher mentioned above. http://www.fz-juelich.de/nic-series/volume23 Monte Carlo Simulation of Polymers: Coarse-Grained Models J. Baschnagel, J. P. Wittmer, and H. Meyer Institut Charles Sadron, 6, rue Boussingault, 67083 Strasbourg Cedex, France E-mail: baschnag, jwittmer, hmeyer @ics.u-strasbg.fr f g A coarse-grained simulation model eliminates microscopic degrees of freedom and represents a polymer by a simplified structure. A priori, two classes of coarse-grained models may be distinguished: those which are designed for a specific polymer and reflect the underlying atom- istic details to some extent, and those which retain only the most basic features of a polymer chain (chain connectivity, short-range excluded-volume interactions, etc.).
    [Show full text]