HydroGEN Kick-Off Meeting University of Colorado, Boulder STCH. Charles Musgrave, University of Colorado - Boulder November 14, 2017 NREL, Golden, CO

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1 HydroGEN Kick-Off Meeting University of Colorado, Boulder STCH Charles Musgrave, University of Colorado - Boulder November 14, 2017 NREL, Golden, CO

2 HydroGEN Kick-Off Meeting Computationally Accelerated Discovery and Experimental Demonstration of High- Performance Materials for Advanced Solar Thermochemical Hydrogen Production Charles Musgrave, PI, University of Colorado, Boulder Alan Weimer, Co-PI, University of Colorado, Boulder Project Vision Utilize machine-learned models coupled with ab initio thermodynamic and kinetic screening calculations to accelerate the RD&D of new STCH materials Project Impact In Phase I we will demonstrate the accuracy of thermodynamic and kinetic models for predicting the STCH properties of new materials Award # Year 1 Funding EE $0.25M HydroGEN: Advanced Water Splitting Materials 2

3 Innovation and Objectives Project history This project builds on prior collaborative computational and experimental work at CU Boulder which demonstrated the viability of new spinels for STCH Barriers Material structure and hightemperature stability may be difficult to predict using DFT Traditional calculations of reaction barriers using DFT are too slow for highthroughput screening Proposed targets Metric State of the Art Proposed Computational Validation H 2 productivity Temperature N/A Ceria: 130 μmol/g (1500 C/1000 C) T RED 1500 C ΔT 700 C Matching expt and comp. thermo. and kinetic properties 200 μmol H 2 /g T RED 1450 C ΔT 400 C Partnerships NREL Stephan Lany DFT defect calculations SNL Tony McDaniel Stagnation flow reactor experimentation SNL Eric Coker High-temperature XRD NREL Technoeconomic analysis HydroGEN: Advanced Water Splitting Materials 3

4 Project Tasks: BP1 Objective: Develop a materials-by-design approach utilizing materials informatics, machine learning, and ab initio calculations to accurately predict thermodynamic and kinetic properties of STCH materials and experimentally demonstrate the effectiveness of these models Task 1.0: Machine learning prediction of material stability and phase transitions 1. Assemble training set 2. Assemble descriptor library 3. Train, validate, and test model Task 2.0: Thermodynamic screening of active materials 1. Computational ternary material screening 2. Accelerated development and optimization of doped materials Task 3.0: Kinetic screening of active materials 1. Explicit computational modeling of kinetics 2. Identify transition state characteristics 3. Rapid identification of kinetically active materials 4. Inverse design for doping of reactive materials Task 4.0: Experimental demonstration of active materials 1. Experimental thermodynamic testing 2. Experimental kinetic testing HydroGEN: Advanced Water Splitting Materials 4

5 Technology Innovation 312 compounds DFT calculations performed at 0 K, but stability is temperature dependent Free energies of compounds exhibit systematic trends suggesting that machine learning may be used for predicting temperature dependent stability HydroGEN: Advanced Water Splitting Materials 5

6 Technology Innovation Goldschmidt tolerance factor correctly predicts perovskite formation with 74% accuracy, leading to calculations being performed for wrong phase Material stability and vacancy formation energy depend on crystal structure, magnetic ordering, and charge state Utilization of machine learning for perovskite stability and incorporation of these factors into DFT screening will lead to more rapid and accurate modeling of STCH material thermodynamic properties HydroGEN: Advanced Water Splitting Materials 6

7 Technology Innovation Kinetics must also be considered to screen new materials Tradeoffs between thermodynamic and kinetic properties demonstrated in existing STCH materials Traditional ab initio approaches to calculate kinetic properties are too slow for highthroughput screening Develop rapid screening approaches to accelerate kinetics screening of materials Traditional approach Image 0 Image 1 Image 2 Image 3 Image 6 Image 5 Image 4 Important images for rapid kinetics screening HydroGEN: Advanced Water Splitting Materials 7

8 Team and Roles Charles Musgrave: Project PI Overall technical oversight Point person for communication with DOE Alan Weimer: Co-PI Technical oversight for experimental material testing Aaron Holder: Senior Personnel Computational technical advising Samantha Millican: Graduate Student Thermodynamic materials screening Ryan Trottier: Graduate Student Kinetic material screening Chris Bartel: Graduate Student Machine learning Zachary Bare: Graduate Student Kinetic material screening HydroGEN: Advanced Water Splitting Materials 8

9 Effective Leveraging of the EMN Resource Nodes EMN node capabilities Lab Personnel Capability NREL Stephan Lany First Principles Materials Theory for Advance Water Splitting Pathways SNL Tony McDaniel Virtually Accessible Laser Heated Stagnation Flow Reactor for Characterizing Redox Chemistry of Materials Under Extreme Conditions SNL Eric Coker High-Temperature X-Ray Diffraction (HT-XRD) and Complementary Thermal Analysis NREL Multiple Techno-Economic Analysis for Hydrogen Production Preliminary work has begun with computational nodes Meetings held with experimental nodes to establish capabilities HydroGEN: Advanced Water Splitting Materials 9