Computational Science for Energy Wanda Andreoni Centre de

Computational Science for Energy Wanda Andreoni Centre de Calcul Atomique et Moleculaire (CECAM) Ecole Polytechnique Federale – Lausanne www. cecam. org Trieste, May 31 2010

Computational Science: main domains of application New powerful algorithms, better software (& hardware) are needed… n to help advancement of knowledge (basic science) n to design new solutions: from materials & processes to device architectures n n to establish an intelligent management of power generation and distribution systems to monitor/control/forecast “green” operations.

Outline Materials science and chemistry n n n Solar Energy Hydrogen and Water Novel batteries Nuclear CO 2 capture and sequestration Modeling and simulations : other applications

The Sun as Source of Energy MODELLING & SIMULATIONS needed (i) to improve on and design new materials; (ii) to monitor, improve on and guide materials processing; (iii) to optimize system-device integration & architecture; (iv) to optimize performance of PV power supply systems (e. g. sizing).

The Science of Photovoltaics Class Issues (examples) Status of modeling Needs for simulations a-Si defects degradation rich literature large-size long-time CIGS/Cd. Te defects, doping. . structure growth Ab initio improved algorithms HPC Organic Hybrid fundamental mechs model calculations exciton & carriers and theories generation & migration degradation (polymer) QD Multi Exciton Generation physical insights debate on mechanism new algorithms ? Combination with experiment: crucial for model development.

a-Si based solar cells n n Advantages mature technology material is abundant low cost Disadvantages - less efficient than Xtal - degrades easily under illumination (Staebler. Wronski effect)

a-Si: H Why are experiments not sufficient? Hydrogen often eludes experiments; fast dynamics What can simulations provide? Proofs of models; new possible scenarios; dynamics What type of simulations? Classical molecular dynamics, electronic structure calculations, and synergy (Car-Parrinello method and alike) Potentials? Sampling?

Cd. Te- and CIGS-based PV n n Advantages relatively cheap thin-film technology Disadvantages complex structure scarcity of core elements Issues Defects & impurities: generation, diffusion Nature of interfaces (dependence on deposition method) Carrier transport also through interfaces Role of Grain Boundaries

What can atomistic simulations do more? More accurate prediction of energy gaps, defect levels • Study of interfaces is lacking structure and composition inter-diffusion • Study of grain boundaries formation and role • Study of the effect of temperature & stress conditions Models must be of relatively large sizes (at least 1000 atoms) Methods: Combination of classical MD and ab initio simulations Difficulty to obtain reliable interatomic potentials Efficient intelligent sampling of atomic configurations (REMD; Meta. Dynamics etc) Accurate and efficient algorithms for high-performance computing •

PV Materials Processing n n Modeling is complicated ; it may require multiscale (from atomistic to continuum) but also sophisticated optimization procedures. Need for robust algorithms development (simulations and analysis)

System: Integration & Design n New design problems for PV require the combination of tools and methodologies from electronic and photonic technologies. Maxwell equations & models of the electronic behavior (carrier generation, collection and transport) – Technology-Computer-Aided-Design n Algorithm development required for integration of different methodologies for hierarchical optimization (multi-parameter)

Photocatalysis I for hydrogen production via water splitting (also for air and water purification; surface self-cleaning and self-sterilizing…) Typical catalyst: Ti. O 2 Challenges & need for simulations • Catalysts in the visible • Avoid modification of the “catalyst” • Avoid use of sacrificial reductants or oxidants (see Kohl et al. Science 324, 74 (09))

Photocatalysis II Do we really understand what happens at the water/Ti. O 2 interface? “wet electrons” K. Onda et al. , Science 308, 1154 (05)

Innovative Batteries Li-air aprotic batteries Oxygen through an air cathode: an “unlimited” cathode reactant ! Non-aqueous electrolyte avoids corrosion • Light, small, cheap • No self-discharge • Long-time storage

Li/Air: Research Questions & Topics

Computational Models and Tools Battery research combines the three most challenging aspects of computational physics: ** non-equilibrium, multiphase and multiscale (in space and in time) ** => A complete model may require 100’s of Petaflops (Exascale) computing.

Nuclear power: safety issues Examples Reactors : materials under extreme conditions; aging Fuel cycle : recycling of minor actinides Nuclear waste : safe storage Structural materials : Understanding interaction of dislocations with irradiation defects (e. g. the microstructure) is necessary to predict steel hardening under irradiation. Fuels : Understanding the chemistry of actinides is vital to optimize actinide extraction and complexation Reactor materials aging : Corrosion, fatigue, fracture…

Hierarchical multi-scale simulation of nuclear fuel MD simulation of radiation damage Materials science engineering scale linkage Atomistically-informed phase-field approach for void nucleation and growth & fission-gas behavior Atomic/electronic level (Newton’s laws) Radiation damage, micro-structural mechanisms and materials parameters Continuum level Continuum mechanics, PDEs, constitutive laws ‘Mesoscale’ (viscous force laws) Effect of microstructural processes (fission gas, voids, cracks, diffusion, …) on thermo-mechanical properties

CO 2 : capture & sequestration (CCS) Challenges (examples) I. Find new solvents and additives for wet CO 2 capture by scrubbing. Amine absorption not amenable to large scale deployment in power plants e. g. high rate of degradation due to oxidation and salt formation; high energy penalty for amine regeneration. II. Accelerate mineral carbonation for permanent CO 2 fixation as carbonate. Increase the reaction rate is crucial to obtain an industrial viable process. E. g. aqueous mineral carbonation: accelerate the rate of CO 2 hydration and of silicate dissolution

CCS: a multi-scale multi-physics problem

Basic and general needs for C. S. n Higher accuracy Electron excitation spectrum Defect energies Rates of chemical reactions Rates for diffusion in complex systems… n n More realistic models of complex systems Multi-scale methodologies High Performance Computing often crucial ! Close collaboration with experimental research

Advanced modeling & simulations for … future technologies of power generation & distribution (e. g. smart GRID) n Powerful and novel algorithms to optimize planning, to characterize behaviour & forecast response (short - and long-term) under various scenarios (multiple temporal and spatial scales). n Better software and visualization capabilities to transform grid management to real-time automated state. n n New demands to technology will require the aid of computer-aided design. Examples: for large-scale energy storage and low-loss transmission. Designing and simulating a network so that it works in real time represents a grand computational challenge on an unprecedented scale.

PV-based Power Supply Systems n n n PV Stand-alone, Grid-connected or Hybrid Note: HPV includes other RE sources (typically wind, hydrogen, diesel) Need: Optimize system engineering Modeling of single components Control and coordination System sizing Prediction of maximum-power point Methods: Conventional approaches: empiric, analytic, numeric, statistical Innovative approaches: Artificial Intelligence methods (ANN, GA, FL…) ANN=artificial neural network GA=genetic algorithm FL=fuzzy logic

CECAM and C. S. for Energy Our activities n First workshop on “Critical materials issues in inorganic photovoltaics” W. A. , Claudia Felser, Tanja Shilling, June 2008 n Brainstorm meeting on “Computational Science for Energy ” W. A. and Claude Guet, Divonne, May 09

CECAM Workshops on ENERGY & ENVIRONMENT (2010) 2010 n Materials modelling in nuclear energy environments: state of the art and beyond M. Samaras, R. Stoller, R. Schaeublin, M. Bertolus, April 26 -29 (Zurich) n Gas separation & gas storage using porous materials L. Valenzano, C. O. Arean, C. M. Zicovich-Wilson, May 17 -19 (Lausanne) n Electronic-structure challenges in materials modeling for energy applications N. Marzari and A. Rubio, June 1 -4 (Lausanne) n Ab initio electrochemistry M. Sprik and M. Koper , July 12 -14 (Lausanne) n Actinides: Correlated electrons and nuclear materials L. Petit, B. Amadon, S. Miller, June 14 -16 (Manchester) n Computational carbon capture B. Smit, S. Calero, T. J. H. Vlugt, July 26 -28 (Lausanne) n Simulations and Experiments on Materials for Hydrogen Storage S. Meloni, S. Bonella, G. Schenter, October 11 -14 (Dublin)

THANK YOU FOR YOUR ATTENTION

Knowledge advancement and design of new solutions There is a strong need for advanced materials, novel processing routes and innovative devices in the generation and exploitation of alternative energies. Control and design imply substantial progress in understanding. Simulations (computational materials science and chemistry) using accurate methodologies and HPC are often invoked as critical auxiliary tools to experiment. n Examples: increase lifetime of nuclear reactors; tailor materials properties for better performance, guide materials processing to lower cost & help system-level integration. n New methods for carbon sequestration rely on understanding that only HPC simulations can provide n Use of bio-fuels relies on the understanding of bio-energy conversion mechanisms (plant and microbial processes) for which HPC simulations are mandatory n Coupling climate and environmental modeling is a must to make a step forward.

Free Energy Diagram of Metal-Oxide catalyzed Recharge Goal: Oxygen Gas + Li Metal O + 2(Li+ + e) 2 O 2 Li I + (Li+ + e) M-O- G - e U 0 Start Li 2 O 2 (s) = U – U 0 Li+ + Li. O 2 Time - 2 e U 0

Computational Example 1 – Redox Reaction on Cathode n Realistic, ab-initio modeling of Oxygen Redox Reaction in aprotic environments n n Ab-inito calculation of possible reaction pathway for the oxygen reduction reaction on a catalytic surface. By Manos Mavrikakis, U. Wisconsin et. al. , performed at NCSA and SDSC Tera. Grid systems. (Fuel Cell) the challenge is the reverse (recharge) reaction Realistic, ab-initio modeling of Oxygen Redox Reaction in aqueous environments n similar to fuel-cells, but

Computational Example 2 – n Realistic Interfaces and Transport modeling of electrolyte/electr ode interfaces n Purpose n Combined Quantum-mechanical and molecular Mechanical Model of electrolyte/electrode interface Model by T. Jacob, Univ. Ulm n Model the solvent and ion transport mechanisms which is a very

Experiments Meta. Dynamics: A. Laio and F. L. Gervasio, Rep. Prog. Phys. 71 (2008)

Materials Science