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G
Reports from Breakout Session Groups
A key component of the Workshop on Information and Communications
was the set of four breakout sessions that enabled individual input by workshop
participants on the four themes of the workshop: discovery, interfaces, challenges,
and infrastructure. Each breakout session was guided by a facilitator and by the
expertise of the individuals as well as the content of the plenary sessions (Table
G-1~. Participants were assigned one of three groups on a random basis, although
individuals from the same institution were assigned to different breakout groups.
Each breakout group (color-coded as red, yellow, and green) was asked to ad-
dress the same set of questions and provide answers to the questions, including
prioritization of the voting to determine which topics the group concluded were
most important. After every breakout session, each group reported the results of
its discussion in plenary session.
The committee has attempted in this report to integrate the information gath-
ered in the breakout sessions and to use it as the basis for the findings contained
herein. When the breakout groups reported votes for prioritizing their conclu-
sions, the votes are shown parenthetically in this section.
185
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186
TABLE G-1 Organization of Breakout Sessions.
APPENDIX G
Breakout Session Group Facilitator Session Chair Rapporteur
Discovery Red D. Raber J. Ottino P. Jurs
Yellow P. Cummings J. Tully E. Vera
J. Hempel
Green J. Jackiw K. Lipkowitz C. Breneman
Interfaces Red P. Cummings J. Tully G. McRea
J. Hempel
Yellow J. Jackiw K. Lipkowitz R. Friesner
Green D. Raber J. Ottino P. Westmoreland
Challenges Red J. Jackiw K. Lipkowitz G. McRea
Yellow D. Raber J. Ottino L. Rahn
G. Martyna
Green P. Cummings J. Tully P. Westmoreland
J. Hempel
Infrastructure Yellow-Red D. Raber J. Tully M. Tuckerbag
(with G. McRea)
Green-Red J. Jackiw K. Lipkowitz P. Gund
J. Ottino C. Breneman
DISCOVERY
What major discoveries or advances related to information and communica-
tions have been made in the chemical sciences during the last several decades?
Red Group Report
Prioritized List:
Analytical instrumentation and data visualization (11)
Computational materials science (10)
Process design and optimization (6)
Drug design and bioinformatics (5)
Environmental chemistry and modeling (5)
Materials applications (4)
Advances in quantum mechanics (3)
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APPENDIX G
Yellow Group Report
Molecular Simulation of Chemical Systems
187
Materials, polymers, advanced materials, crystals and nanostructures, simu-
lation techniques (DFT, Monte Carlo and MD)
Microfabrication by Chemical Methods (e.g., lithography)
Synthesis, design, and processing of new materials
Computer-Aided Drug Design
QSAR, molecular modeling
Numerical Simulation of Complex Chemical Processes Involving Reaction
Transport
Ozone layer, combustion, atmospheric chemistry, chemical vapor deposition
Process Simulation, Optimization, and Control
Supply-chain optimization, plant design, and remote control of plants
Green Group Report
Electronic Structure Theory (9)
Common language of electronic structure theory, validating density func-
tional theory, standardization of Gaussian basis sets, semiempirical methods,
B3LYP functional (Becke's three-parameter hybrid functional using the LYP
correlation functional), well-defined procedures for QM algorithms
Research Simulation Software for All Science (8)
Gaussian software, archiving chemical information, software development
for the community, process control and process design software
Computer-Aided Molecular Design (7)
Combinatorial chemistry for drug study
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188
Chemical Kinetics and Dynamics (5)
APPENDIX G
(coupling electronic structure theory with dynamics (direct dynamics), kinet-
ics from first-principles rate constants, stochastic simulation theory
Potential Functions and Sampling Methods (4)
Monte Carlo and molecular dynamics for statistical thermodynamics, poten-
tial energy functions and applications, discovery of applicability of potential en-
ergy functions, reactive empirical bond order potentials
Data-Driven Screening (2)
Application of statistical learning theory and QSPR, data mining and combi-
natorial high-throughput methods, virtual high-throughput screening
Synthesis of Materials for Computing (1)
Photolithography polymers, metallization, optical fibers
INTERFACES
What are the major computing-related discoveries and challenges at the in-
terfaces between chemistry-chemical engineering and other disciplines, includ-
ing biology, environmental science, information science, materials science, and
physics?
Red Group Report
Targeted Design
How do we go backward from the desired specifications and functions to
identify the molecules and processes?
A Matrix View Is Needed
Rows (discovery opportunities): biosytems modeling, proteins, drug design,
materials design, environmental modeling (life-cycle analysis), molecular elec-
tronics, nanosciences
Columns (challenges to achieving discoveries): combinatorial search algo-
rithms, data assimilation, multiscale modeling, structure-function and structure-
activity relationships, large-scale computation, how to create interdisciplinary
teams as well as support the critical need for disciplinary research
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APPENDIX G
189
Results: the matrix is dense (every cell is a yes); people issues are critical;
opportunities exist for both methodology development and application; there are
many rows and columns, so the challenges are prioritization and maximizing the
intersections; we need to show relevance, examples and benefits of what we are
trying to do.
Yellow Group Report
Molecular Design (8)
Modeling at an atomic scale, small molecules vs. larger structures (one level
of multiscale modeling); examples include design of drugs, nanostructures, cata-
lysts, etc.
Materials Design (18)
More complex structures, additional length scales, property prediction (e.g.,
microelectronics devices, microfabrication, polymers, drug delivery systems,
chemical sensors)
Multiscale Modeling (17)
Incorporates all length scales, important in all disciplines; examples include
modeling of a cell, process engineering, climate modeling, brain modeling, envi-
ronment modeling, combustion
Data Analysis and Visualization (6)
Analysis of high throughput data, seamlessness of large datasets, modeling
of high-throughput datasets, information extraction, component architectures for
visualization and computing
New Methods Development (6)
Computer Science and Applied Math
Green Group Report
Human-computer interface, expert systems, software engineering, high-per-
formance computing, databases, visualization
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190
Environmental Science
APPENDIX G
Fate of chemicals, computational toxicology, climate change, speciation
Biology
Protein structure-function; protein sequence-structure; networks (kinetic,
regulatory, signaling); bioinformatics; dynamics in solvent environment
Physics
Chemical dynamics, scattering theory, molecular astrophysics, high-tempera-
ture superconductors
Materials Science and Engineering
Polymer property prediction, large-systems modeling (mesoscale simula-
tion), nanostructures, corrosion
Earth Science
Water chemistry; binding of chemical species to ill-defined substrates; high-
temperature, high-pressure chemistry; atmospheric chemistry
Crosscutting Issues
The issues are pervasive: every matrix entry is a yes.
Matrix columns (computing-related problem areas): software and interfaces
(expert systems, development and maintenance and incentives); systems biology
(chemistry, physics, and processes complexity); molecular dynamics with quan-
titative reactive potentials; chemical and physical environment of calculations
(e.g., solvation); simulation-driven experiments; special-purpose computing hard-
ware; closing the loop for computational chemistry with analytical instruments
and methods; database access, assessment, and traceability
Matrix rows (disciplines that interact with chemistry-chemical engineering):
computational science and mathematics; environmental science; biology; phys-
ics; materials science and engineering; earth sciences
CHALLENGES
What are the information and communications grand challenges in the chemi
cat sciences and engineering?
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APPENDIX G
Key Message
Reducing the time to solution
Some General Points
191
Red Group Report
There are many possible information and communications challenges; soci-
ety: no chemicals in this product; education: communicating excitement; techni-
cal: information gathering and exchange; new disciplines can emerge or have
emerged from taking a systematic approach to knowledge storage and representa-
tion (e.g., genomics); if we are to be successful in our recommendations we need
to address the challenges, opportunities and benefits (particularly with examples).
A Matrix View
Rows: grand challenge problems; columns: information and knowledge-
based methodologies
Discovery Opportunities (Rows)
Simulating complex systems, cell, combustion, polyelectrolytes and com-
plex fluids, atmospheres, hydrogeology, catalysts under industrial conditions,
drug design, protein-DNA interactions, protein-protein, RNA folding, protein
folding, metabolic networks, conformational sampling
Knowledge Issues
Knowledge database and intelligent agents; assimilating sensor-measure-
ment-information data, data exchange standards (XML, etch; representation of
uncertainties in databases, building and supporting physical property archives
(NIST, Webbook, etch; collaborative environments
Methodology Issues
Model-based experimental design, long time scale dynamics, virtual mea-
surements, force field validation and robustness, quantum dynamics, dispersion,
excited states
Systems Approaches to Complex Problems
Intelligent and distributed databases (the "chemistry Google"), next-genera-
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92
APPENDIX G
tion "laboratory notebooks" for models, databases, programs, and experimental
data, next-generation databases, communication, sampling, and interaction across
scales; this is not a new issue.
YELLOW GROUP REPORT
Long-Term Challenges
Comprehensive data and computational problem-solving environment (12~:
seamless chemical information, computer human interface
Simultaneous product and process development (12~: for example, drug dis-
covery, cradle-to-grave chemical production
Design of new molecule given required functionality (11~: design lowest-
energy consumption path, proof of concept for computational drug design
Virtual cell (4)
Design biocompatible materials (3~: artificial tissue and bone, artificial or-
gan, posttranslational protein modification
Short Term Challenges
Design-controlled directed self-assembly (10)
Self-assembly of protein in membrane (5)
Chemical plant on a chip (4)
Predict crystal structures (small organics) (4)
Computational electrochemistry (4)
Accurate free-energy calculation of medium size molecules (3)
Green Group Report
Molecularly Based Modeling
Electronic structure methods for 104+ atoms, binding energies in arbitrarily
complex materials, molecular processes in complex environments, computational
tools to study chemical reactions (first principles, limits of statistical reaction rate
theories), simulators with quantum dynamics for quantum degrees of freedom
Systems Issues
Integration of applied math and computer science to obtain more powerful
software tools for the chemical sciences and engineering, modeling the origin and
development of life, alternative chemical bases for life (other worlds), computa-
tional disease modeling, design of big-inspired materials, global climate control
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APPENDIX G
193
(the earth as a chemical factory similar to Groves' chip as a chemical factory),
modeling of atmospheric chemistry
Measurements
Incorporation of computational chemistry methods into instruments, virtual
measurement, enabling ubiquitous sensing, in vivo biosensors
Education and Usage Issues
Translate molecularly based modeling into education, expert systems for
method choices (precision in context of time and cost)
INFRASTRUCTURE
What are the issues at the intersection of computing and the chemical sol
.
ences for which there are structural challenges and opportunities in teaching,
research, equipment, codes and software, facilities, and personnel?
Yellow-Red Group Report
Topics To Be Addressed
Teaching, research, equipment, codes and software, facilities, personnel
Teaching-Related Infrastructure Issues
Computational chemistry courses, interdisciplinary computational science
courses, software engineering (not just programming), test problems and design case
studies, support for software maintenance, modeling- and simulation-based courses;
top two issues: modeling-and-simulation course, and mathematics training
Research-Related Infrastructure Issues
Multiscale modeling algorithms, representation and treatment of uncertain-
ties, standard test cases (software, experiments), funding of interdisciplinary re-
search, shared instruments; top two issues: multiscale modeling algorithms and
funding of interdisciplinary research
Equipment-Related Infrastructure Issues
Access to high-performance computational facilities; high-bandwidth access
to instruments and computers; collaborative environments; clusters and tightly
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94
APPENDIX G
coupled, diverse architectures; assimilation of information from sensors;
interoperability and portability; distributed databases; shared resources (equip-
ment, software, computers); access to a diverse set of architectures; top two is-
sues: access to a diverse set of architectures, and interoperability and portability
Codes and Software-Related Infrastructure Issues
Integration of multivendor software systems, software maintenance (labora-
tory technicians), open source, code and data warehouses, component architec-
tures, interoperability, security; top two issues: component architectures and open
source code
Personnel Related-Infrastructure Issues
Poor computer and software engineering skill levels of incoming students,
tenure and promotion of interdisciplinary people, training of chemists with chemi-
cal engineering concepts, people in the pipeline, attracting interest of computer
science community; top two issues: tenure and promotion of inter-disciplinary
people, and people in the pipeline
Green-Red Group Report
What Parts Are Working?
Commercial software companies serving this area: computational chemistry,
chemical processes
Chemist-chemical engineer collaboration works well where it exists
Modern programming tools have speeded code development
Networking: Internet high-speed connectivity
What Parts Are Not Working ?
Commercial software efforts limited by market size: limited new science,
porting to new machines, commercialization can kill technology, coding complex
programs still very difficult, high-performance computing centers are not gener-
ally useful, proposal process too restrictive, DOE centers working better than
NSF centers
Education of new computational chemists and chemical engineers inad-
equate: undergraduate-graduate curriculum needs to address these subjects, ap-
plications and scientific programming (Colorado School of Mines Engineering
Web site is doing this)
Academic code sharing and support mechanisms poor: code development
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APPENDIX G
195
not supported by grants, no money or staff for maintenance and support, duplica-
tion of effort on trivial code; need open source toolkits and libraries
Databases and knowledge bases: not generally available except in
bioinformatics, need standards and data validation
Cross-discipline collaboration and teamwork inadequate: overspecialization
in graduate school departments
What Payoffs Are Expected ?
Proof of profit demonstration that simulation methods can make difference
in business outcome, "home run" in drug discovery, more incorporation of meth-
ods into educational programs (better understanding of scope and limitations of
methods), make specialists in industry more connected and useful (leveraging
current staff), better communication and teaming of chemists and chemical engi-
neers (more joint appointments, maybe combining departments), commonality in
understanding and language between users and nonusers
How Can Community Assure Reliability, Availability, and Maintenance of
Codes ?
Recognize contributions of coders, support full life cycle of codes, agencies
should fund code development and maintenance as part of grants, demonstrate value
of simulation codes, funding incentives for cross-disciplinary software creation
Representative terms from entire chapter:
multiscale modeling
