1. Transformational Opportunity Summary John Sarrao 12/04/14

2. Assignment Spatially and Temporally Dependent Chemistry and Materials (Gordon Brown): -bio matter: Gordon Brown, Matt Tirrell & Jennifer Lewis -hard/soft interface: Bill Tumas & Juan de Pablo Beyond Ideal Systems: Interface behavior and heterogeneity: Roger French & Tony Rollett & Gordon Brown Harnessing coherence in light and matter: Margaret Murnane, Nora Berrah & Sue Coppersmith Ushering in a New Era in Control Science: Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing: Jamie Sethian & Sharon Glotzer Probing and Mapping Matter Across Multiple Scales: Making and Exploiting transformative advances in imaging capabilities:Tony Rollett, Margaret Murnane & Emilio Mendez)

3. Efficient Synthesis for Tailored Properties Control at the Level of Electrons Correlated Systems Energy and Information On the Nanoscale Systems Away From Equilibrium Bio Inspired Matter Intersection of Hard and Soft Matter Interface behavior and Heterogeneity Harnessing Coherence in Light and Matter Spatially and Temporally Dependent Chemistry and Materials: Graham’s circle

4. New Opportunities have emerged that have their foundations in the Grand Challenges Spatially and Temporally Dependent Chemistry and Materials Synthetic mastery of materials Intersection of hard and soft matter Bio-inspired matter Beyond Ideal Systems: Interface behavior and heterogeneity Understanding real matter under realistic conditions Spatio-temporal Evolution and dynamics Quantum/Classical Systems: Mesoscale Complexity Science of Heterogeneity/Defects that Limit Lifetime Performance Harnessing coherence in light and matter Coherent control over the entire electromagnetic spectrum from THz to x-ray Manipulating Matter to Elucidate New States Of Matter Coherent Phonon/Light Interactions Ultra efficient communication and quantum computing Breakout #2 - Crabtree

5. Bio-Inspired and Hybrid Soft Matter (Matt) Soft materials can have information content, built into the nature and positioning of reversible interactions among their constituents, which guides them to stable, precisely organized, yet adaptable, structures with unprecedented properties. Hard/soft interface (Bill) The combination of soft and hard materials can generate functionality common to neither and along with the ability to move away from empirical optimization to actual design across a range of morphologies (e.g. layered, nanoscale and mesoscale) and composition could have a transformative impact on energy conversion, energy efficiency and energy storage. the soft-soft interface (Juan) Complex, functional materials must necessarily integrate multiple elements to capture molecular-level signals, amplify them, and transduce them into observable macroscopic responses. Soft materials are particularly promising at achieving such functions in that minute perturbations can induce massive molecular reorganizations. Interfaces are the gateways that allow or impede that transfer of information from one material element to another. The molecular structure of soft interfaces can be tailored to control the transfer of species or information across them, as well as their response to external cues, be they physical or chemical. Spatially and Temporally Dependent Chemistry and Materials 5

6. Hierarchical structures – biology has done this in spades and uses the cell, tissue, organ, organism theme in remarkably diverse functional ways. Artificial soft matter is not as hierarchical - we don’t know how to construct such exquisitely complex hierarchies, Functionality – biology makes functional hierarchical structures, using natural selection to eliminate all the non-functional variations. We try to design function into structures without natural selection as a guiding principle, this seems to be a very slow and inefficient approach (and throwing out the non-functional variations is time consuming and frustrating). Dynamics – biology derives functionality in part from complex dynamics, artificial soft matter makes much less use of dynamics. Can we build functionality from assembling “simple” dynamics like stimulus response into more complex dynamics like catalytic enzymes and controlling muscles with nerve signals and ultimately with thoughts? Artificial soft matter seems very far away from biological soft matter in exploiting dynamics, is there something to learn from biology here? Interactions – biology uses all kinds of interactions in a single functional unit, including hydrophilic-hydrophobic, electrostatics, hydrogen bonding, thermal fluctuations, steric configurations,... We have not explored these interactions thoroughly enough to put them to use as effectively as biology. Can we learn from biology? 6

7. Synthetic soft materials have yet to exploit the full range of interactions that give biological materials remarkable properties. Achieving this level of organization requires potency and precision placement of interactions that challenge chemical synthesis. New advances in polyelectrolyte complexation, programmable self-assembly and controlled polymerization, and post-polymerization, synthetic methods are making biological levels of control over materials organization conceivable. These methods are exploiting, and will continue to exploit, powerful synchrotron and neutron scattering sources, and well as new microscopy and spectroscopy techniques, that are emerging. Computational advances are leading to better predictive design capabilities. Programmable self-assembly. Charge complexation as a route to new materials. Surface localization, templating and texture. Hybrid organic-(bio)-inorganic composites. The construction of new materials based on a repertoire of reversible interactions resembling those found in biology will enable: structurally self-correcting/self-repairing materials; dynamic materials; energy- and signal-transducing materials, and; new materials for information storage and processing. Bio-Inspired and Hybrid Soft Matter 7

8. Hybrid systems comprised of hard, often crystalline, inorganic materials and soft, often amorphous organic, inorganic materials or a combination of inorganic and organic materials represent a new frontier for creating new multifunctional systems. Recent examples of hard/soft hybrid systems relevant to energy technologies include perovskite and organic photovoltaics, organic light emitting diodes, lithium ion batteries, PEM fuel cells, and multifunctional catalysis. These materials and structures can be both thermodynamically stable configurations but more commonly they are in a metastable state in hybrid systems. Development of Synthetic Approaches for Hybrid Materials Advanced characterization tools including in-situ methods Advanced theory and computation and integration with experiment. 1) These materials and systems are increasingly important in a wide range of critical energy technologies including solar energy conversion, solid state lighting, polymer based fuel cells, and Li polymer batteries among others; 2) Computational materials science is beginning to be able to look at complex hybrid materials both in terms of static and dynamic properties and 3) New synthetic methods including combinatorial approaches are enabling the controlled synthesis of increasingly complex materials, both thermodynamically stable and metastable materials, (e.g. atomic layer deposition) and 4) Analytical instrumentation including in-situ measurement capabilities at DOE beam lines is increasingly able to examine the structure, opto-electronic and chemical properties over a range of length scales. Hard/soft interface 8

9. Soft materials are uniquely susceptible to weak forces.. Interfaces can coordinate communication between distant elements of a material, and give rise to emergent behaviors that remain unknown. New advances in theory, simulation, and characterization, offer unprecedented opportunities to understand soft interfaces at a molecular level of detail. Through modern chemical synthesis and nanofabrication techniques, that understanding could be translated into synthetic interfaces that incorporate and even surpass some of the functionality of biological interfaces, thereby ushering an era of highly integrated multifunctional soft materials design. Characterization Synthesis Directed, Hierarchical self-assembly Theory and simulation the soft-soft interface 9

10. Beyond biology – we may aspire to surpass the mechanisms and achievements of biology, for example by simplifying complex protein structures with more straightforward analogs that perform better. We do this in some cases, usually with hard matter (computers, steel for structures) but not for soft matter (animal brains, water splitting). For the present state of artificial soft matter development, the example of biology is well beyond our reach and we have much to learn from it. However ultimately biology may be too small a vision, we may be able to do much better. 10

11. Spatial and temporal heterogeneity in energy materials, such as at interfaces and across populations, give rise to most of their real-world benefits. At the same time this complexity and its evolution confound our fundamental physical modeling of these systems. Elucidating the rich, non-ideal, behavior and characteristics of energy materials is essential to guide their real-world implementation and success. New approaches to understand interfacial structure, chemistry, dynamics and evolution in under real-world conditions and timeframes is the grand opportunity. Heterogeneity dominates real-world energy materials, be it structural/spatial or temporal, and give them their function and properties while at the same time determining their degradation over lifetime Instead of simply studying one sample as a representative of a heterogeneous population, population-based epidemiological studies can be added to lab- based small sample number experiments, and with this approach, heterogeneity, variance, fluctuations, all become a natural part of the data, modeling and science Beyond Ideal Systems: Interface behavior and heterogeneity 11

12. Heterogeneity, disorder and complexity are essential to functionality. They also control degradation, when the functional configuration, however complex, heterogeneous and disordered, acquires detrimental defects that inhibit its functionality. What are materials systems examples? Solar cell requires complexity, heterogeneity and disorder to absorb sunlight, excite electrons across a bandgap, and separate electrons at a pn junction (an interface or gradient). Degradation sets in when defects disrupt any of these three functions. Structural steel acquires its strength through heterogeneity of composition and structure, small amounts of carbon and other trace impurities and a fine polycrystalline matrix give steel its strength, oxidation eventually degrades this microstructure. Biological solution to degradation is replacement or self-healing: create new tissue to replace damaged or aged tissue, replace machinery of photosynthesis damaged by UV sunlight with new machinery every 30 minutes, allow aged members of species to die and be replaced with “baby” members. 12

13. Degradation science aims to prevent or delay degradation by observing, understanding and controlling it. Is there an aspirational goal here? Design functional materials whose functionality does not degrade? Functionality is based on average behavior, degradation on extreme or rare event behavior Challenge is to model complex systems, to capture functionality based on ”good” complexity, and degradation based on “bad” complexity. We are much better at modeling homogeneous “perfect” systems such as crystals or molecules, but not heterogeneous systems with parts that exchange charge, spin, matter or energy across interfaces or along gradients. Do we want our materials/systems to be “smart,” monitoring themselves for degradation and making decisions to fix the degradation? Could be done by monitoring functionality quantitatively and calling for repair or replacement of specific parts when functionality falls below a threshold. Materials Genome models at the material or component level. Is modeling the functionality enabled by heterogeneous elements interacting across interfaces/gradients the required next step? With computers getting ever more powerful, is this within reach? Is this simply what we mean by “multiscale modeling” or does functionality modeling imply something else? MGI is the “tip of the iceberg,” functionality modeling of complex heterogeneous systems whose components interact across interfaces is the next visionary and aspirational goal. 13

14. Surprisingly, our ability to exploit coherence in light and matter is far more robust than we initially realized. One important new understanding is the role of topology in protecting coherence, particularly in materials systems with strong coupling between orbital and spin electronic degrees of freedom. Another unanticipated advance is that a single ultrafast laser can manipulate the quantum wavefunction of a radiating electron. In quantum information science, a goal is to control many-particle systems at the quantum limit for quantum simulation and information applications. Being able to implement full control of large-scale quantum-coherent systems has the potential to revolutionize technology in areas such as information processing, sensor technology, or energy generation using optimized solar cells. Harnessing coherence in light and matter 14

15. Revolutionary and cross-cutting capability advances are transformative Ushering in a New Era in Control Science: Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing simulation, algorithms, information extraction and data analytics Data Science Numerical Modeling, Simulation Intuitive computation Thermodynamics, Information Entropy transport and processing of information Mathematical models to solve inverse problems (Inverting the Design Arrow) Theory->Experiment/Data and Data->Theory Visualization Probing and Mapping Matter Across Multiple Scales: Making and Exploiting transformative advances in imaging capabilities Electron orbital changing in a reaction (attosecond imagining) “seeing” Better x-ray and electron sources Integration of Multi-modal Imaging of Functional Systems Imaging Science From Atomic and Electronic to Meso, Space & Time Scales Breakout #3 - Sarrao

16. A “perfect storm” of computational capabilities are poised to greatly accelerate our ability to find, predict, and control new materials, understand complex matter across a range of scales, and steer experiment towards illuminating deep scientific insights. The past ten years have seen a torrent of new theoretical models, profound algorithmic advances that cross traditional boundaries, groundbreaking techniques to process and understand data, and an explosion in computing resources, from smart phones and fast sensors to dedicated high performance supercomputers, Taken together, they offer opportunities to profoundly advance the science required to control matter and energy to meet the nation’s energy needs. three elements are coming together to usher in this new era of control science. They include: (1)Theory and models: new frameworks to capture physical phenomena; (2)Algorithms: new methods to solve relevant equations and sift through data to find new patterns; and (3)Advanced compute resources Ushering in a New Era in Control Science: Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing 16

17. Making and Exploiting advances in imaging capabilities is a national priority because of its transformative impact on materials discovery, accelerating the introduction of new materials, and solving long-standing challenges in the linkage between the structure of matter and its behavior, especially considering heterogeneity. find ways to reconstruct raw data into usable images, to analyze the results, to integrate data from multiple sources, and to continue to extend the techniques to regimes that address the manifold scientific puzzles that confront us: Attosecond measurement Energy sensitive mapping 4d characterization Probing and Mapping Matter Across Multiple Scales: Making and Exploiting transformative advances in imaging capabilities 17