Information and Communications Challenges for the Chemical Sciences in the 21st Century (2003) / Chapter Skim
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3. Opportunities, Challenges, and Needs
3. Opportunities, Challenges, and Needs
Pages 21-28
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From page 21... ...
Major progress on issues of societal policy ranging from energy to manufacturability, from economic viability to environmental impact, and from sustainable development to responsible care to optimal use of matter and materials might all follow from the integrated capabilities for data handling and system modeling provided by advances in information technology. This field is beginning to see the development of cooperative environments, where learning, research, development, and design are carried out utilizing both modeling and data access: this cooperative environment for understanding may be the most significant next contribution of IT within the chemical sciences.
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From page 22... ...
Computational Methods: The implementation of theoretical models within software packages has now become excellent for certain focused problems such as molecular electronic structure or simple Monte Carlo simulations. Very large challenges remain for extending these methods to multiscale modeling in space and time.
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From page 23... ...
Targeted design, as well as our understanding of simple systems, can profit by investigation within such a holistic research environment. Although the exponential increases in capability and utilization cannot continue forever, the advances already attained mean that the capabilities available to the chemical sciences are not limited to commodity computing and the constraints of more, faster, and cheaper cycles.
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From page 24... ...
Tools for semiautomatic generation of new graphical user interfaces could facilitate calling or retrieving data from these extant programs. Eventually, one can envision an object-oriented approach that would include interfaces to a suite of components for integrating chemical science modeling tasks such as electronic structure calculation, smart Monte Carlo simulation, molecular dynamics optimization, continuum mechanics flow analysis, electrochemical process modeling, and visualization.
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From page 25... ...
We need to develop new methods in green chemistry for manufacturing in the twenty-first century, and we need to accelerate environmental remediation of sites around the world that have been polluted over the previous century. Examples include o incorporating computational methods into sensors for real-time analysis and assimilating sensor-measurement-information data in simple yet technically correct formats for use by public policy decision makers; and o expanding our environmental modeling efforts especially atmospheric and oceanic modeling to account for the impact and fate of manufactured chemicals; to assess how changes in air and water chemistry affect our health and well-being; and to develop alternative, efficient, and clean fuel sources so that we need not rely on imported hydrocarbons as our major energy source.
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From page 26... ...
These societal issues include providing stewardship of the land, contributing to the betterment of human health and physical welfare, ensuring an informed citizenry through education, facilitating more thoughtful and informed decision making, and protecting and securing the society. The chemical sciences need to develop data-driven, natural teaching- and information-capture methods, preferably including some that do not require equation-based algorithms (what might be called natural learning)
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From page 27... ...
The community needs to incorporate advanced theoretical ideas to generate nearly rigorous techniques for extending accurate simulations to deal with phenomena over broad ranges of time and space. It also needs to attack such methodological problems as model-based experimental design, virtual measurement, quantum dynamics, integration with continuum environments, dispersion energetics, excited states, and response properties.
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From page 28... ...
containing modules for specific capabilities; · develop reliable error estimators for computational results; . develop enhanced methodology for data mining, data management, and data-rich environments, because databased understanding and insights are key enablers of technical progress; and · develop improved methods for database management, including assurance of data quality.
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