Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Zhigang Mei Project Name: DFT_ALD_SiC Division: NE Project title: Computational Design of Novel Precursors for Atomic Layer Deposition of SiC Associated funding: LDRD Innovate Other Systems: Science: The goal of the proposed work is to use a multi-scale computational approach (DFT+KMC) to down-select novel chemical precursors for atomic layer deposition (ALD) of SiC and identify the ideal process of ALD, and to verify the proposed precursors and reaction routines using experiments. In addition to gaining a better fundamental understanding of the underlying kinetics of the precursor-surface reaction, this research will help to identify new precursors and optimize deposition conditions for ALD of SiC at low temperatures. Low temperature ALD is preferred, as it will limit interdiffustion of the substrate and coating, reduce thermal stresses within the coating, and will maintain the initial desirable microstructure of zircaloy. The implications of the proposed activities can be extended beyond nuclear materials to other areas of interest, such as semiconductors. Project description: Computational screening of precursors for ALD SiC: Due to the large chemical space for silicon and carbon containing molecules and complex nature of ALD surface reactions, our screening procedure of potential precursors for ALD SiC is composed of the following three steps: (1) Evaluate the thermodynamic feasibility of the formation of SiC from various Si and C precursors; (2) Predict the energetics and kinetics of the ALD half-reactions for the proposed precursors; (3) Validate the down-selected precursors by experiments. From the PubChem Compound Database, we found more than 900,000 silicon-containing molecules and significantly more molecules for carbon precursors. By filtering the PubChem database with proper descriptors, such as molecular weight and boiling point, there are about 200,000 silicon-containing molecules left. As the first step of the screening procedure, we are constructing a large chemical reaction database of silicon and carbon-contain ing molecules with thermodynamic properties calculated by high-throughput DFT calculations. To automate the construction of the database, we have developed a computational framework using python, which can (1) get molecular information from the PubChem Database, (2) extract Canonical SMILES description for each molecules by OpenBabel, (3) obtain initial geometries as input file for Gaussian by OpenBabel, (4) perform DFT B3LYP geometry optimization calculation by Gaussian, (5) perform TD-DFT calculations with 6-31+G(d) for optimized geometry using B3LYP/6-31G(d), and (6) upload TD-DFT/DFT B3LYP input/output files to online database. With everything automated, we expect that our database will have more than 100,000 entries of silicon and carbon-containing molecules by the end of FY17. With the large chemical reaction database, we will apply machine learning algorithm to develop chemical reaction prediction models using novel fingerprint for molecules. The developed machine lea rning model can be used to screen the entire PubChem Compound database for all the possible precursors. In the second year, we will continue to add more entries for our silicon and carbon molecule database using high-throughput DFT computations in order to identify potential new precursors for SiC using machine learning. Our initial tests show that the computational time for a typical precursor pair such as SiCl4 and CCl4, takes about 100 cpu-hours. Due to the huge number of precursors for Si and C, we expect to calculate 5000 pairs of precursors. The total computational time we requested for FY18 is 500,000 cpu-hours. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 500000 Q1: 125000 Q2: 125000 Q3: 125000 Q4: 125000 Justification: Efficient DFT code Gausian will be used to do high-throughput DFT calculations. Storage requirements: 1 TB Thank You, The LCRC Accounts System