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: CLS Project title: A Molecular Model of Raney Ni Associated funding: None Other Systems: None Science: Raney nickel[1] is a nanostructured catalyst that is used in a variety of industrial processes. Due to its catalytic activity at room temperature, it is used for reduction of benzene, conversion of nitro compounds to amines, and for desulfurization of thioacetals to hydrocarbons. It has a characteristic porous, amorphous structure. The initial form of Raney Ni is 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[2] 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 ad hering together[3]. 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: The initial NiAl compositions can range from 40 to 70 wt% Ni [2,4,5]. Freel at al.[2] determined the mean pore diameter to be in a range between 2.5 to 10 nm. The basis for approaching the structure of the active form is the characterization studies of Raney Nickel [2,6,7,8] by Anderson’s group and others that yield a qualitative picture of Raney Nickel. The void volume of these particles ranges from 0.05 to 0.15 cm3/g [1]. X-ray diffraction [3] and X-ray Absorption Spectra4 show that local structure of Raney Ni is similar to FCC Ni. Fouilloux [3] gave a qualitative description of Raney Nickel as a spongy material composed of 100 nm nano-agglomerates of smaller than 2.5 to 15 nm nanocrystals. Several modeling studies of grain boundaries use molecular dynamics to generate structures. Van Swygenhoven and Caro [9] used a stochastic procedure that starts with randomly placed seed particles with the rest of the space filled with a Voroni construction. Kebli nski [10] randomly placed seed particles in a liquid metal and then quenched the melt to generate a model crystal with grain boundaries. These procedures yield a model of a solid composed of nanocrystalline grains. These studies focus on modeling the structure of the final catalyst. However, a critical question is, how the initial alloy composition influences the final catalyst performance. The aim of the our work is (a) to demonstrate how a porous structure of Raney Ni can be modeled with random pore distribution while taking the initial precursor composition into account and (b) to investigate to what extent the activity of the final catalyst depends on the composition of the the precursor alloy. In the previous phase of the project, molecular dynamics (MD) simulations have been performed on fusion and blues 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 [11] 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 in a similar way as described by Noya et al. [12] For one configuration of each composition, an additional NPT simulation of 1 ns at room temperature was performed to check the convergence of the density and the cell size. The analysis of theses simulations showed that t he composition of the precursor has a significant influence on the density and cell parameters, 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. To establish a simulation protocol for modeling the porous structure of Raney Ni, we have focused on one structure with the initial alloy composition of 50 wt % Al and 50 wt % Ni which corresponds to the commonly used composition of the precursor. After quenching to room temperature, Aluminum was removed. For studying the influence of the remaining amount of Al on the pore formation of the final catalyst, two percentages of remaining Al have been considered: (a) 0% and (b) 5 % Al were left for modeling the active form of the catalyst. These structures were then optimized using 5000 steps Steepest Descent. A short 200 ps NVT simulation was performed from which the structures of 10 snapshots were extracted. These structures were optimized and used as starting point for a 10 ns NVT MD simulations at 300 K. In order to ensure a statistically relevant sampling, initial velocities were randomly chosen for each system. Finally, a 20 ns NPT simulation was performed to equilibrate the structures under physically meaningful conditions. In order to study the influence of the system size on the size of the created pores, a 2x2x2 supercell was created after quenching for both the 0% and 5% Al structure containing 163,840 atoms and 181,856 atoms, respectively. For both systems, first a 10 ns NVT simulation was performed followed by a 10 ns NPT simulation. The MD simulations and final structures were characterized using the MAPS platform [11] for studying the evolution of the density, free volume, and free surface and the Zeo++ software [13,14] for analyzing the pore size and pore size distribution. The structural properties of our modeled structures are in very good agreement with experimental data. In particular, an average value of 4.1 ± 0.4 g/cm3 was obtained for the density and of 43 ± 5 Å for the maximum pore size, which is both well within the experimental range of 3.5 - 7 g/cm3 and 20 - 100 Å, respectively. The simulations also indicate that the presence of a small percentage of Aluminum stabilizes the pores as the comparison of the evolution of the pore size and the pore size distribution of the final 0% and 5% Al structures suggests. The analysis of the 2x2x2 supercell structures showed the same trend with respect to the structural characteristics corroborating the validity of our simulation approach [15]. In the previous phase of this project we have established a simulation protocol that allows to create realistic model structures of the final catalyst and to take the initial composition of the precursor into account. For the validation of our simulation approach we have focused on only one composition (50 wt% Ni). In the next phase, the simulation protocol shall be applied on the other three compositions (40,60,70 wt% Ni) for investigating the influence of the composition on the pore formation. Since we have observed that the remaining Aluminum content influences the evolution of the structural characteristics and in particular the pore size distribution, we plan to perform two sets of calculations with different percentages (0% and 5%) of remaining Aluminum content for each of the compositions. First, a 5 ns NVT simulation will be performed for inducing the pore formation and afterwards a 15 ns NPT simulation for relaxing the porous structure. In this way, we can gain a fundamental understanding about the formation of nanostructures and their dependence on the composition. The computational time needed for performing these MD simulations and analyzing the trajectories will be considerable: The model structures contain more than 65,000 atoms and in total six 20 ns MD simulations (3 compositions x 2 percentages, 5 ns NVT+ 15 ns NPT) need to be performed. According to the LAMMPS Cu benchmarks , 100 steps for a system size of 32,000 atoms would take 0.3 s (fixed CPU time) on 512 cores. A 20 ns trajectory (20,000,000 steps) for 32,000 atoms would take 8,600 core-hours and a 65,000 atom system about 17,500 core-hours. 110,000 core-hours will be needed for the MD simulations of the various model structures differing in composition and Aluminum content. The final porous structures obtained from the MD simulations will be used for studying the influence of the initial composition on the catalytic process. For this purpose, the catalytic hydration of ethene on the Ni surface will be considered. A random configuration of a mixture of ethene and dihydrogen within the simulated porous Raney Nickel system will be generated using MAPS Amorphous Builder. Then, a short MD simulation will be performed to create initial structures for further calculations at ab initio level. To understand the effect of the porosity on the catalytic reaction, we plan to use a bottom-up approach in which the system size will be systematically increased starting from a smaller model system and ending up with the realistic microporous Raney Ni. The commonly accepted mechanism suggests that the reactants are first adsorbed on the Ni surface and then the hydrogens are transferred to the carbon atoms one after another, before the product is released from the surface. The different reaction energies will be evaluated as well as the influence of the local environment. Molecular and periodic methods within the density functional theory (DFT) will be used to evaluate how the reaction energies can be determined best. For our simulations, we will either carve out clusters of increasing size from the MD simulations or Ni surfaces of increasing slab depth and size will be created. Such an approach should allow us to find a minimum system size for which the catalytic effect of t he environment is represented with reliable accuracy. The calculations will be performed using ab-initio software package such as CP2K, Abinit, Quantum Espresso, and NWChem. Plane wave-based and atomic orbital-based methods typically require a careful testing of pseudopotentials and basis sets, respectively. Therefore, a detailed validation study regarding the convergence of reaction energies with respect to the method and parameters used will be performed, before studying the reaction in detail. It requires about 4.5 core-hours to compute the energy of a 50 atom metal cluster with CP2K. It will require 250 energy evaluations or 1 thousand hours to compute the NEB path for the reaction on a metal cluster. To compare the barriers for a 100 different sites on our models of Raney Ni and Ni surfaces will require 100,000 core-hours. Overall, 210,000 core-hours will be needed over a period of 12 months for performing the in silico experiments for the project. [1] Raney, M. US patent 1,563,787 (1925). [2] Freel, J., Pieters, W. J. M. & Anderson, R. B. “The structure of Raney nickel: I. Pore structure.” Journal of Catalysis 14, 247–256 (1969). [3] Fouilloux, P. et al. “A study of the texture and structure of Raney nickel,” Journal of Catalysis 25, 212–222 (1972). [4] Devred, F. et al “The genesis of the active phase in Raney-type catalysts: the role of leaching parameters”, Applied Catalysis A: General 224, 291-300 (2003). [5] Barnard, N.C. “A quantitative investigation of the structure of Raney-Ni catalyst material using both computer simulation and experimental measurements ”, Journal of Catalysis 281, 300–308 (2011). [6] Freel, J., Pieters, W. J. M. & Anderson, R. B. The structure of Raney nickel: II. Electron microprobe studies. Journal of Catalysis 16, 281–291 (1970). [7] Robertson, S. D. & Anderson, R. B. The structure of Raney nickel: IV. X-ray diffraction studies. Journal of Catalysis 23, 286–294 (1971). [8] Robertson, S. D. & Anderson, R. B. “The structure of Raney nickel: V. Partial activation of the catalyst.”, Journal of Catalysis 41, 405–411 (1976). [9] Van Swygenhoven, H. & Caro, A. “Plastic behavior of nanophase metals studied by molecular dynamics.” Physical Review B 58, 11246–11251 (1998). [10] Keblinski, P., Wolf, D., Phillpot, S. . & Gleiter, H. “Structure of grain boundaries in nanocrystalline palladium by molecular dynamics simulation,” Scripta Materialia 41, 631–636 (1999). [11] MAPS version 3.4, Scienomics, Paris, France (2014). [12] Noya, E.G., “Amorphization of Ni–Al alloys by fast quenching from the liquid state: a molecular dynamics study”, Journal of Non-Crystalline Solids 298, 60–66 (2002). [13] Martin, R.L., Smit, B., Haranczyk, M. J. Chem. Inf. Model. 52, 308-318 (2012). [14] Willems, T.F., Rycroft, C.H., Kazi, M., Meza, J.C., Haranczyk,M. Microporous Mesoporous Mater. 149, 134 - 141 (2012). [15] Schweizer, S., Chaudret, R., Low, J.J., Subramanian, L. “Molecular Modeling and Simulation of Raney Nickel: From Alloy Precursor to the Final Porous Catalyst” submitted to J. Phys. Chem. C (2014). [16] http://lammps.sandia.gov/bench.html#eam. Industry partnership: 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. Robin Chaudret, Research Scientist at Scienomics, has a PhD in Computational Chemistry from the Université Pierre et Marie Curie of Paris. He has been doing research in the fields of organic, organometallic and bio chemistry. He applied and developed various tools to analyze and simulate the structure and reactivity of different systems. His ability to perform multiscale simulations (Quantum, Classical and QM/MM) allows him to cover a broad range of research areas such as homogeneous and heterogeneous catalysis, biochemistry or bioinspired chemistry, surfaces, etc. During his PhD and postdoc experiences he developed several collaborations with Paris or Montpelier Universities in France and Duke University in North Carolina and organized and participated to different meetings and published several papers. 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: 210000 Q1: 52500 Q2: 52500 Q3: 52500 Q4: 52500 Justification: Storage requirements: Thank You, The LCRC Accounts System