Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: John J. Low Project Name: Raney_Nickel Division: MCS Project title: A Molecular Model of Raney Ni Associated funding: None Other Systems: None Science: Raney nickel is a nanostructured catalyst which is used in a variety of industrial processes and organic syntheses such as hydrogenation reactions. The basis forms a NiAl alloy which is transformed into the active form by leaching out aluminum which leads to the characteristic porous structure of the final catalyst. The porosity depends on the initial composition of the NiAl alloy which is typically prepared by melting nickel together with aluminum and subsequent quenching. The quenching process induces the formation of different NiAl phases which react differently with the caustic and thus affect the pore formation in the final catalyst. Experimental studies indicate that the sponge-type structure of Raney Nickel grains is formed by a collection of crystallites adhering together. Since the structure of both the precursor alloy and the active form of Raney Nickel is determining the catalytic activity and performance, it is crucial to gain a detailed understanding of the structural properties at the atomistic level. The goal of this project is to leverage molecular modeling to provide critical insights into how the composition of the precursor alloy affects the porosity and thus activity of Raney Nickel. Project description: During the first phase of the project, molecular dynamics (MD) simulations have been performed on fusion using the LAMMPS software package in order to analyze the influence of the initial composition of the alloy on the structural properties of the precursor. Model structures for four different compositions (40/50/60/70 wt% Ni) have been created using Scienomics MAPS platform by randomly replacing Ni and Al in the corresponding fractions in a NiAl3 supercell. In order to ensure a statistically relevant sampling, five initial starting configurations have been generated for each of these model structures. The structures were equilibrated at 2000 K and then cooled down to room temperature over 1 ns as described by Noya, et al. [J. Non-Crystal. Solids 298, 60–66 (2002)]. The MD simulations performed so far provided insight to what extent the initial configuration and compositions affects the structure of the precursor. The analysis density and cell parameters of the final structures indicates that the composition has a significant influence, while the configuration has only a very minor impact. In general, the density, cell parameters, total energy and volume over the simulation time show a consistent behavior. The final structures as obtained from the quenching simulations will serve as basis for additional MD simulations and equilibrated for 1 ns at room temperature to ensure convergence. The trajectories will be analyzed with respect to the formation of nanostructured clusters based on a next neighbour search, for which MAPS scripting facilities will be used. The trajectories will be analyzed with respect to the formation of nanostructured clusters. In addition we plan to ues the open source program RINGS (http://rings-code.sourceforge.net] will be used to analyze local structural environments and voids. From each model, Al will be removed and only 5-10 % Al will be left which corresponds to the experimentally observed final catalyst composition. These porous models will be minimized, which will only require a few hundred core-hours, and afterwards analyzed with regard to pore size, pore distribution, and accessibility from surface for the various compositions and initial config urations to explore trends that can be linked to the catalytic performance. In this way, we can gain a fundamental understanding about the formation of nanostructures in the precursor alloy in dependence of the initial composition. In the laboratory, the precursor is activated though treatment with sodium hydroxide leaching out the majority of the Al and leading to structural re-arrangements of the Ni bulk. This process will not be simulated as it is beyond the scope of the present project. Instead, we will focus on modeling the structure of the final catalyst. To provide a reliable description of the nanoporous structure, large system sizes will be required which will also allow to capture effects of grain boundaries that a supposed to be important for the chemistry of the final catalyst. The computational time needed for performing MD simulations and analyzing the trajectories of all 20 models will be considerable. Each model structure contains more than 65,000 atoms. According to the LAMMPS Cu benchmarks[ http://lammps.sandia.gov/bench.html#eam], 100 steps for a system size of 32,000 atoms would take 0.3 s (fixed CPU time) on 512 cores. A 1 ns trajectory (1,000,000 steps) for 32,000 atoms would take 500 core-hours and a 65,000 atom system about 1250 core-hours. 25,000 core-hours will be needed for the simulations of the 20 alloy model structures. Characterization studies of Raney Nickel by Anderson’s group and others which yield a qualitative picture of Raney Nickel. [J. Cat. 16, 281–291 (1970)] The void volume of these particles ranges from 0.05 to 0.15 cc/g. X-ray diffraction and X-ray Absorption Spectra show that local structure of Raney Ni’s is similar to FCC Ni. Fouilloux gives a qualitative description of Raney Nickel as a spongy material composed of 100 nm agglomerates of smaller 2.5 to 15 nm nanocrystals. Our model of Raney Nickel starts with 500 randomly packed 12.5 Å spherical nanocrystallites of Nickel in a 100 nm sphere. The procedure of Lubachevsky, et al. will be used to generate random packing of the spheres.[J. Stat. Phys. 64 501–524 (1991)] Since random close packing has a density of approximately 0.63, this model will have a void volume (0.06 cc/g) similar to Raney Ni. The initial positions of the atoms in the Ni nanospheres will be created from a randomly oriented and shifted FCC latt ice. The final structure will be determined through simulated annealing and the EAM potentials as implemented in LAMMPS. Our model should be qualitatively similar to experimental structure of Raney Nickel. These structures will be tested by comparing calculated properties from the predicted structure to the experimentally determined pore size distribution, heterogeneities from small angle X-ray scattering and line broadening in X-ray diffraction. Several modeling studies of grain boundaries use molecular dynamics to generate structures. Van Swygenhoven and Caro used a stochastic procedure that starts with randomly placed seed particles with the rest of the space filled with a Voroni construction.[Phys. Rev. B 58, 11246–11251 (1998)] Keblinski randomly placed seed particles in a liquid metal and then quenched the melt to generate a model crystal with grain boundaries. [Scr. Mater. 41, 631–636 (1999)] These procedures yield a model of a solid composed of nanocrystalline grains. We will follow the spirit of these modeling approaches by randomly placing seeds of Ni crystallites in a gas (or liquid) of Ni atoms and use simulated annealing to generate a low energy structure. Raney Nickel, which has a void volume of 0.1 cc/g, is essentially half void by volume. A simple model would be to seed a gas with a density half that of Ni metal. The gas will condense on the seed crystallites and create an agglomerate with the appropriate void volume. Since the condensation is exothermic, a Nose or Andersen thermostat will keep the temperature below the melting temperature (1728K) of Ni during the simulation. Starting at 1500 K and scaling the temperature to 0K over 100 picoseconds will quench to a low energy structure through simulated annealing. Since the low energy structure will create particles which have the density of Ni metal, the voids between the particles should have half the volume of the initial volume. The number of crystallites should control the size of the nanocrystals formed during simulated annealing. A careful choice of initial conditions should lead to the desired size of nanocrystals and void volumes. The RINGS program will allow a systematic and analytical analysis of these structures. A 100 nm droplet of Ni with half the density of bulk Ni is a model of Fouilloux’s description of Raney Nickel.[J. Cat. 25, 212 (1972)] The scale of this calculation would be significant. The model would be initiated by randomly placing 500 seed crystallites (Ni55 clusters) in a hundred nanometer sphere and then randomly inserting 25 million atoms into the space between the seeds. The embedded atom method as implemented in LAMMPS can handle simulated annealing of models of this size. The Cu EAM benchmark on the LAMMPS website shows that 100 steps of molecular dynamics for 16 million atoms could be done on 512 cores of a Xeon/Myrinet cluster in 21 seconds. [http://lammps.sandia.gov/bench.html#eam] A 100 picosecond trajectory (100,000 steps) would take 60 hours (30,000 core-hours). Fusion or Blues have faster processors and an InfiniBand interconnect that will yield better performance. The estimated CPU requirements derived from timings for the Xeon/Myrinet cluster is an upper bound. We anticipate repeating this calculation a few times with three different compositions requiring 100,000 core-hours. Our initial calculations will use a periodic model containing a million atoms and 20 seeds to verify this approach will give morphology similar to Raney Nickel. The test will involve comparing calculated properties from the predicted structure to the experimentally determined pore size distribution, heterogeneities from small angle X-ray scattering and line broadening in X-ray diffraction. [J. Cat. 23, 286 (1971), Cat. Today 163, 13–19 (2011)] If this model does not yield a satisfactory result we will implement and test a stochastic Voroni procedure. [Phys. Rev. B 58, 11246–11251 (1998)] These calculations will require a few thousand core hours and will not exhaust our allocation before developing a working model. Once we have established a procedure that generates a model of a porous solid composed of nanocrystalline grains, we will carry out calculations on a more realistic 50 million atom droplet model of Raney Ni. 50,000 core hours will be needed for these experimen ts with smaller periodic models. We will use EAM to model hydrogen adsorption on our model of Raney Nickel. The model will have monolayer coverage (one hydrogen atom per surface Ni atom). The surface will be defined by Ni atoms with coordination less than twelve. Simulated annealing will be use to find a low energy distribution of hydrogen on the surface of Raney Ni. The predicted binding energies, vibrational frequencies and geometries will be used to classify the chemisorption sites to a smaller number of representative sites. 50,000 core-hours will be required for the chemisorption models. The pore size, pore distribution, and the accessibility of the droplet model and chemisorbed model will be compared with the results obtained for the precursor models. This strategy will allow to explore to what extent the nanostructural agglomerations in the precursor are reflected in the porous active form. The generalized chemisorption sites will form the basis of smaller periodic models. We will use the smaller models to model hydrogenation of benzene on Raney Nickel with an electronic structure program like abinit, bigdft, cp2k or qbox. These calculations will be done in a future project. Overall, 200,000 core-hours will be needed over a period of five months for performing the in silico experiments for the project. About Scienomics Scienomics was established in 2004 and with selected partnerships with the best-of-the-breed (e.g.: Sandia National Labs, Max Planck Institute, Fraunhofer Institute, University of Illinois, Demokritos, University of Shanghai), Scienomics’s MAPS platform offers a unique and powerful blend of multiscale and multiparadigm modeling and simulation modules. With high quality science and industrial solution oriented applied research, Scienomics has garnered collaborative projects with companies in the areas of energy and green chemistry, alternate fuels, catalysis, polymers, biodefense, auto exhaust, etc. Biography of Relevant Members John J. Low, Principal Computational Science Specialist at Argonne National Laboratory has a Ph.D. in Chemistry from the California Institute of Technology. After graduation John worked at UOP LLC, a Honeywell Company. His work at UOP focused on modeling and characterization of catalysts and adsorbents used in the petrochemical industry, hydrogen storage and carbon sequestration. John supports the computational chemistry applications at high performance computers and performing research on Li-Ion batteries, converting biomass to fuels, and nuclear materials at Argonne. He is expert in quantum chemistry, molecular dynamics and Monte Carlo methods on high performance computers. Dr. Sabine Schweizer, Senior Scientist at Scienomics has a Ph. D. in Theoretical Chemistry from University of Tuebingen, Germany. Her work experience is in the area of applying quantum chemical methods to large and small molecules. Her experience is particularly useful in the areas of homogenous and heterogenous catalysis, where energetics and dynamics play an important role in the reactions. She has experience working with multiscale hybrid methods combining quantum chemical and classical methods which makes her particularly versatile in applying Scienomics’ multiscale, multiparadigm MAPS platform to study hard materials such as surfaces, coatings, semiconductors, solar cells, etc. Dr. Lalitha Subramanian, Chief Scientific Officer and VP of Services has 18 years of experience providing insight into chemical systems and processes that are of critical interest to industry. Following her Ph. D. in Chemistry, her post-doctoral work was with Prof. Roald Hoffmann (Nobel Laureate) at Cornell University. She has been a leading architect of solutions in the areas of alternate energy, catalysis, materials design, and process optimization. She has worked on numerous proprietary projects for diverse companies and in this pursuit; she has delivered product enhancements, process optimization and cost savings to her customers. Lalitha maintains a broad range of partnerships in oil & gas, chemical, personal care, materials, semiconductors, automotive, aerospace, and pharmaceutical industries. She has co-authored a book on software techniques used in Materials Science published by CRC Press, 2005. She continues to present invited lectures and has numerous scientifi c publications. Recent References: Kimberly-Clark Corporation use Scienomics software and services for predicting properties of new polymer systems (2012). US Army Research Lab use Scienomics software and services for proprietary research on polymeric systems (2012). Materials Engineering Research group at Purdue University used Scienomics software suite for studying the crosslinking process of thermosetting polymers (2010). Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 200000 Q1: 50000 Q2: 50000 Q3: 50000 Q4: 50000 Justification: Thank You, The LCRC Accounts System