1. Models, Algorithms & Software
2. Hardware Cyberinfrastructure
3. Databases and ChemInformatics
4. Education Infrastructure
5. Remote Chemistry

     Several models, algorithms, and software developments are needed to carry out computational and theoretical chemistry that will enhance or be enhanced by developments in cyberinfrastructure. Nearly all problems at the forefront of the chemical sciences require bridging across multiple length and time scales for their solution. Techniques are needed to reversibly map quantum-mechanical scales to atomic scales, atomic scales to mesoscales, mesoscales to macroscales. These mapping techniques may not generalize across all chemical systems, materials, and processes. Consequently, development of coarse-grained models and methods for bridging models across length and time scales is a high priority.

     Models and Algorithms. At the level of quantum-mechanical (QM) methods, accurate order-N methods are needed to enable the determination of ground and excited states of systems containing on the order of several thousand atoms, and to calculate system dynamics, kinetics, and transition states. Another approach worth pursuing is the application of ensemble and sampling methods to QM problems with greater sophistication and quality than is now possible. In the electronic-structure calculation of condensed phases, there is a need to include relativistic effects for heavy metals and to develop methods beyond density functional theory (DFT). It is not clear at this point how to systematically improve DFT and related methods.

     At the classical, atomistic level, more-sophisticated force fields, including reactive force fields and empirical potentials that can realistically model heterogeneous, disparate materials and complex (e.g., intermolecular) systems, will be beneficial. Development of these approaches would benefit from access to systematic databases of QM and experimental data and from new tools to automate force-field optimization. Databases should be provided in standard data formats with tags, and it should be easy to use tools to access data for validation and parameterization of models. Similarly, improved mesoscale models and methods for the consideration of heterogeneous, multiphase, multi-component, and multi-material systems are needed. Time-scale meshing is an issue, for example, in combustion and environmental flow (e.g., through soil) problems. Additionally, methods are needed to model both chemical and physical processes at the cellular level and such basic processes as solvation.

     Hardware resources that can be brought to bear on a particular scientific challenge are not hard to identify. Yet these resources are the underpinnings of all the layers of the hierarchy of cyber-enabled chemistry (CEC). The areas discussed in this report – data and storage, networking, and computing resources – do not cover the full gamut of what constitutes the entire base infrastructure, but it is in these three areas that the need for sustained funding and opportunistic possibilities is most acute.

     Data and Storage. A low-latency, high-bandwidth, transparent, hierarchical storage system (or systems) will be needed for facilitating federated-database access and archival storage. The system interfaces must account for the mapping of data, information, and knowledge at all levels. Significant infrastructure research will be required to federate and use the multitude of databases that are and will be both available and required for CEC. The fidelity and pedigree or provenance of that data must be maintained. With the neare-xponential growth of data now occurring, the situation will only get worse. The hardware should be tuned to allow for ubiquitous access to the literature, and to existing chemistryspecific databases such as the PDB, thermochemistry, NIST, and other chemical-sciences and engineering databases. The hardware system functions should include the collection of user-based and automatically generated metadata both from site-specific systems – e.g., the NSF-funded supercomputer centers – and from applications run at those centers. Site specifics from experimental facilities and their associated instruments need to be included, as well. Finally, access to data must be carefully controlled at the user, group, facility, and public levels. Users must be able to determine what data gets pushed out to the various levels within the user community and how.

     The micro-architecture of all these resources must feature balanced characteristics among the many subsystems available (e.g., memory bandwidth and latency must match the computational horsepower, and cache efficiency is critical to achieve high performance). Parallel-subsystem characteristics (e.g., inter-node communication systems and secondary storage) must also be balanced. Deployed systems’ characteristics must be appropriate for chemistry and chemical-engineering applications. In addition, programming models and compiler technology must be advanced to increase programmer efficiency. This is an obvious area for cross-collaborative development with other NSF directorates and divisions.

     Visualization systems will become even more critical to the insights and engagement of experts and non-experts alike. It is easy to visualize a handful of atoms, but simulations of protein systems with chemically reactive sites, reactive chemical flows, microscopic systems, etc., will demand improved visualization capabilities, which the chemistry community, in turn, must learn how to develop and use. Visualization tools such as immersive caves, power walls, high-end flat-panel displays, and, most importantly, appropriate software tools will lead the practicing cyber-chemist to new chemical discoveries. Remote visualization of data via distributed data sources is a difficult challenge, but one that must be met.

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However, there still remains a number of standard problems that continue to be wholly or partly unsolved in regards to databases, their management, and their use in the chemical sciences context. Data can exist in database form or in the more amorphous literature or on the Web. Cyberinfrastructure will be needed to provide tools to access data, organize it, and convert it into a form that enables chemical insight and better technical decision making, as well as to facilitate communication to and from non-experts to bridge the gap between scientists and public perceptions. On one level, data can be defined as a disorganized collection of facts. Information can be defined as organized data that enables interpretation and insight. Knowledge is understanding what happens. Wisdom is the ability to reliably predict what will happen. Cyberinfrastructure is essential to move between these levels.

     The activity of validation and consistency is extremely labor intensive, but essential. Tools should be developed that can cross-check data as much as possible. Experimental and computational results can be used for mutual screening. One example is the automatic evaluation of consistency for thermodynamic-property data performed by the NIST Thermodynamics Resource Center. Stored data and information should reference details of how the data was acquired (the “metadata” or “pedigree”) to enable experts to evaluate the data. It would be beneficial to have authors assign uncertainty to published data, or to have sufficient information available for an expert or expert system to quantify uncertainty in a measurement or predictive model. If data is very crude with a high uncertainty (e.g., more than an order of magnitude), this is important for a non-expert to know, as the person may have to engineer a system with greater allowances. Converting non-evaluated data in paper legacy systems into a validated, refereed database is an extremely time-consuming activity that is currently done by experts only in their spare time. Is this a valuable use of an expert’s time? How else can we do it? Are there some aspects of evaluation that could be performed by an expert system? How do we ensure that data published from now on can be readily evaluated and captured? New approaches to these problems is an activity that should be supported.

     Standards will be very important for interoperability and communication, but how to get people to adopt and use these standards? Should journals require them? The demand on experts who evaluate data after it has been published could be relieved by having journals require the entry of raw as well as evaluated data, uncertainty estimates, and similar metadata. Standardization can enable automated data capture. Standardization should be tiered, perhaps consistent with the maturity of the data or medium being captured. Standardization would be very effective in capturing current literature, for example. Raw data has the longest-lasting value and should be archived. Interpretations may change over time as science and understanding progress. Long-term archiving or legacy databases or other collections of knowledge, especially non-electronic ones, require experts to extract information. How will we need to access information in the future?

     Visualization is critical. Data needs to be visualized in a manner consistent with how a person thinks. This may vary to some degree depending upon the field of expertise: i.e., visualization of the same process for a chemist may differ from what is most effective for a physicist, materials scientist, biologist, or lay person. Creative visualization at a fundamental level may be an effective way to bridge vocabulary and conceptual paradigm differences across disciplines.

     Other issues include new database paradigms for collection, archiving, data-mining tools, validation, and retrieval needed to facilitate interdisciplinary collaborations Interoperability between databases at different levels as well as user interfaces will facilitate data mining. Automated dictionaries and translators that will greatly facilitate communication between scientific disciplines and between scientists and the lay public. This will require new software tools.

There is no substitute for critical thinking and the human element. Hence for discovery, the emphasis in the near term should be on tool development to archive, gather, extract, and present data and information in a manner that will enable creative thinking and insight. The development of artificial intelligence to draw conclusions from data and information at this stage may be best suited for collection and evaluation of objective, quantitative information such as property data. This will certainly evolve in the future.

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     Educational activities represent an important component of virtually all topics associated with any emerging cyber-enabled chemistry (CEC) project or initiative. Because of CEC’s multidisciplinary focus and the nature of new science potentially facilitated by it, even individual investigators in single, well-defined subdisciplines of computational chemistry are likely to benefit from educational components that might be developed. Defining the possible scope of these educational endeavors, as broad as they might be, is a helpful first step in this vision-generating process. The prototype audiences for educational efforts can be divided into four groups, though they undoubtedly share some information needs.

     The first group is composed of research scientists, both within chemistry and from allied fields. While disparities certainly exist, by and large the individuals in this group can be described as problem-solving experts with strong motivation and capabilities for learning. Thus, non-chemists may need to become more proficient in molecular sciences, while chemists might require education in computational methodologies and limitations – but all members of this category are probably capable of self-directed instruction given appropriate materials.

     A second category of professionals, who will require education, are established and emerging educators themselves. If students are to be reached, particularly at early points in their studies, those who teach them will need both greater depth and greater breadth of information about the nature of CEC. High-school teachers in particular may face barriers to learning that are associated with missing background (in physical chemistry or mathematics) or concomitant fear of materials that appear to require extensive background in the areas to be understood.

     Educators, either at the high-school or introductory college levels of the curriculum, serve as the conduit for the next vital group of people, students (in the traditionally defined sense). Computationally-related science in general, and CEC in particular, should be infused into the undergraduate curriculum. Inclusion of computational philosophy in the undergraduate chemistry curriculum is important both from an educational perspective, and from a pragmatic one: Future scientists who populate the world of CEC will need longer and more-complete training in scientific computing and modeling methodologies. Computational approaches must be introduced carefully into classroom and laboratories, so as to avoid fueling student misconceptions about chemistry – a possibility probably necessitating continuing educational research as new materials, methodologies, and curricula are developed.

     Cyberinfrastructure Challenges. The educational demands of training for a multidisciplinary CEC environment pose specific challenges and opportunities. It is possible to specify certain educational activities likely to support large-scale development and deployment of CEC in the future, such as instructional materials and software and middleware.

     Instructional materials. Self-training or tutorial materials are an important component of the educational strategies for several of the four groups delineated above. These materials should be developed with several factors in mind. First, materials for learning computational chemistry, visualization tools, and reaction animations already exist; we need to benefit fully from lessons gained in the creation of those materials. Second, in the same way that workflow models for the CEC enterprise require humanfactors research, educational materials must be tailored to the cognitive demands made on the target audiences, the different cognitive capabilities of different audiences, and the fundamental constraints associated with any asynchronous learning environment. Because, for example, online educational materials tend to stress certain innate cognitive skills in the learner, educational research regarding differences in learning styles is likely to be valuable as part of the development process. Ultimately, materials development in CECoriented education may best proceed as multiple small projects, with users competitively “rating” the materials offered. A self-assessment component to calibrate users’ learning gains might also provide important systematic data for educational assessment and overall project evaluation.

     Software and middleware. Educational software and middleware constitute another important area for future development. It will be important to develop interdisciplinary educational materials and programs that include both computational chemistry and morefundamental computational sciences. As CEC works to help practitioners maximize the efficiency of their modeling efforts, the same lessons learned in improving research productivity will probably also enhance new learning materials’ effectiveness. Interfaces to state-of-the-art computational resources for novices should be both intelligent and multilevel. As novice users gain proficiency in specific modeling technologies, the educational interfaces they are using should automatically allow more flexibility for those users and reveal new, more complex modes of the CEC environment. Some students in this category will be expert learners already. The CEC modeling environment’s power as well as its limitations should be emphasized in ways that scientists who are not computer modelers will find useful.

     Specific issues associated with cyberinfrastructure are common to educational efforts as well. Cybersecurity is important across the entire spectrum of CEC deployment, including educational developments. Networking-infrastructure disparities (including those that exist on local scales within most or all educational institutions) may play a bigger role in the educational components of CEC than in its research components, where infrastructure is more nearly uniform. Finally, the changing of hardware availability in the educational environment may render such issues as interoperability of educational materials even more paramount within education than within CEC as a whole.

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     The emerging national cyberinfrastructure is already enabling new scientific activities through, for example, the use of remote computers, the development and use of community databases, virtual laboratories, electronic support for geographically dispersed collaborations, and numerous capabilities hosted as web-accessible services. New virtual organizations are being established (such as virtual centers of excellence) that assemble distributed expertise and resources to target research and educational grand challenges. Cyberinfrastructure research currently being driven by other scientific domains – in areas such as scientific portals; workflow management; computational modeling; and data analysis, visualization, and management – is clearly relevant to chemistry as well.

     However, certain characteristics of the chemistry community – specifically, the broad range of computational techniques and data types in use and the large number of independent data producers – pose unique challenges for remote chemistry. Distributeddatabase federation, sample and data provenance tracking (e.g., as in laboratory information management systems, or LIMS), and mechanisms to support data fusion and community curation of data are particularly relevant to chemistry and thus are areas where this community may drive cyberinfrastructure requirements. In addition, environmental chemistry – which may soon involve experiments drawing data from thousands to millions of sensors – and high-throughput chemistry will be leading-edge drivers for new cyberinfrastructure capabilities.

     While the term “remote chemistry” suggests an emphasis on bridging physical distances, much more challenging gulfs to bridge than distance are, in fact, differences in distributed collaborations’ and organizations’ cultures, levels of expertise, organizational practice and policies, and scientific vocabularies. Close interaction between practicing chemists and information technology developers, iterative approaches to development and deployment, and mechanisms to share best practices will all be critical in developing new remote-chemistry capabilities to meet the needs of a diverse chemistry community.

     Remote communities and practitioners may also be confronted with currently poorly understood social/cultural constraints. For example, members of certain constituencies (e.g., based on ethnicity, race, culture, nationality, age, and/or gender) may adapt to the remote-community concept far more readily after initial strong personal or even face-to-face contact with the other members of the community. Expecting remote communities to develop spontaneously and rapidly in a manner that reflects the current population of interest groups may or may not be realistic. Social research may be necessary to understand how to expand remote communities to accurately reflect national and international demographics.

     Access to and Use of Remote Instruments. Advances in information technologies have made it possible to access and control scientific instruments in real-time from computers anywhere on the Internet. Technologies such as Web-controlled laboratory cameras, electronic notebooks, and videoconferencing provide a sense of virtual presence in a laboratory that partially duplicates the experience of being there. More than a decade of R&D and technological evolution has greatly reduced the time and effort required to offer secure remote-instrument access and proved the viability of remote-instrument services. Instrumentation such as Pacific Northwest National Laboratory’s Virtual NMR Facility has migrated from being research projects to ongoing operations, and setting up new instruments for remote operation can now be as simple as running screen-sharing software or enabling remote options in control software (e.g., in National Instruments’ LabView).

     The numerous benefits provided by access to remote instruments include sharing the acquisition, maintenance, and operating costs of expensive, cutting-edge instruments; broadening the range of capabilities available to local researchers and students; moreeffective utilization of instruments; and easing the adoption of new techniques in research projects and student laboratory courses. While there can be drawbacks to remote facilities – for example, conflicts between the service and research missions of a facility, loss of “bragging rights” and control of instruments, and loss of contact with colleagues at an instrument site – the potential benefits far outweigh the drawbacks.

     Enhanced access to remote instruments would benefit the chemistry community. Remote access to expensive, high-end, state-of-the-art instruments will maximize their scientific impact, serve broader audiences, and allow more widespread use of current generation technologies in both research and education. Technical support for planning and operating facilities will be a key enabler. On the other hand, problems limiting adoption of this new research and education mode are potential users’ and facility operators’ unfamiliarity with state-of-the-art networking and distributed-computing technologies, with best practices developed by current remote facilities, and with the learning curve associated with the use of the software tools themselves.

     Cyberinfrastructure research in support of remote facilities will be needed in several areas, including the continuing improvements in ease of use and support for multiple levels of instrument access (e.g., simplified interfaces for novice users or the ability to allow data collection while prohibiting instrument recalibration), mechanisms for coordinating across experiments (e.g., experiments guided by simulation results or by other experiments, or creating large-scale shared community data resources that aggregate individual remote experiments), and managing distributed facilities (e.g., with instruments and experts in various techniques at multiple facilities).

     Access to and Use of Advanced Computational Modeling Capabilities. Computational chemistry, in all of its forms, has made enormous advances. It is now possible to predict the properties of small molecules to an accuracy comparable to that of all but the most sophisticated experiments. Computational studies of complex molecules (e.g., proteins) have provided insights into their behavior that cannot be obtained from experiment alone. Investments are still required to continue to advance the core areas of computational chemistry. But high-bandwidth networking, remote computing, and distributed data and information storage, along with resource discovery and wide-area scheduling, promise to spark the development of new computational studies and approaches, providing opportunities to solve large, complex research problems and open new scientific horizons.

     Of particular interest here are portals, workflow management, and distributed computing and data storage, especially as envisioned in the notion of the “grid,” whose goal is to couple geographically distributed clusters, supercomputers, workstations, and data stores into a seamless set of services and resources. Grids have the potential to increase not only the efficiency with which computational studies may be performed but also the broader community’s access to computational approaches. In this regard, an important target for the chemistry community will be to develop tools that allow the scientist to couple computational codes together to build complex, flexible, and reusable workflows for innovative studies of molecular behavior.

     Collaboratories. Collaboratories enable researchers and educators to work across geographic and organizational as well as disciplinary boundaries to solve complex scientific and engineering problems. They enable researchers and educators to share computing and data resources as well as ideas and concepts, to collaboratively execute and analyze computations, to compare the resulting output with experimental results, and to collectively document their work. While early collaboratories tended to focus on rich interactions in small groups or lightweight coordination within a community, next-generation collaboratories will be able to operate far more effectively, allowing large groups to organize to tackle grand-challenge problems, form subgroups as needed to accomplish tasks, and publish results that are then made available to the larger community.

     Collaboratories have great potential in chemical research and education, particularly in bringing together researchers from multiple subdisciplines and multiple cultures. In particular, the solutions of many grand-challenge problems in chemistry, e.g., the design of new catalysts and more-efficient photoconversion systems or the integration of computation into the chemistry curriculum, would benefit greatly from the services provided by collaboratories.

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