Mathematical Challenges from Theoretical/Computational Chemistry


EXECUTIVE SUMMARY

Overview

Although much of its discovery process is descriptive and qualitative, chemistry is fundamentally a quantitative science. It serves a wide range of human needs, activities, and concerns. The mathematical sciences provide the language for quantitati ve science, and this language is growing in many directions as computational science in general continues its rapid expansion. A timely opportunity now exists to strengthen and increase the beneficial impacts of chemistry by enhancing the interaction betw een chemistry and the mathematical sciences.

Computational chemistry is a natural outgrowth of theoretical chemistry, the traditional role of which involves the creation and dissemination of a penetrating conceptual infrastructure for the chemical sciences, particularly at the atomic and molecular levels. The mathematical sciences have been indispensable allies and have provided vital tools for that role. Theoretical chemistry has also sought to devise and to implement quantitative algorithms for organizing massive amounts of data from the labor atory, and for predicting the course and extent of chemical phenomena in situations that are difficult or even impossible to observe directly; thus, today it is difficult to classify many lines of research as either "theoretical" or "computational." This report tends toward the term theoretical/computational--any distinction between the two areas is rather misleading because the subject demands both quantitative characterization and conceptual understanding.

Computational chemistry has its roots in the early attempts by theoretical physicists, beginning in 1928, to solve the Schrödinger equation using hand-cranked calculating machines. By the 1950s, with the appearance of digital computers, serious at tempts were being made to obtain highly accurate quantitative information about the chemical behavior of molecules via numerical approximations to the solution of the Schrödinger equation. In subsequent years, thanks to leaps in computing power and algorithms, methods have evolved from those that were used to study 1- and 2-atom systems in 1928, through those that were used to study 2- to 5-atom systems in 1970, to the present programs that produce useful quantitative results for molecules with up t o 10 to 20 atoms. Other chemists, whose research can be accomplished with cruder models of the atom, have pushed this limit much higher. For instance, simpler approximations have long been used in the molecular mechanics approach that make possible the modeling of biological molecules with thousands of atoms.

Recent decades have witnessed a revolutionary expansion in the breadth and capability of theoretical and computational chemistry--with a commensurate rise in optimism regarding the ability of theoretical/computational chemistry to resolve pressing probl ems both of a fundamental scientific character and of clearly practical interest. Those outside the field may not realize that theoretical/computational chemistry, broadly defined, underpins rational drug design, contributes to the selection and synthesi s of new compounds, and guides the design of catalysts. New quantum mechanical techniques underlie the understanding of electronic properties of materials and have advanced the level of precision at which molecules of at least moderate size can be modele d. Furthermore, computational chemistry software is a set of tools used increasingly by chemists of many persuasions. These various abilities and facilities have proved to be very important to U.S. industry, and their advancement would generate even furt her industrial benefits. Engaging problems and deep challenges for mathematical scientists are posed by the needs of theoretical and computational chemists, and the products of mathematical research in these areas could have far-reaching ramifications.

The statement of task given to the Committee on Mathematical Challenges from Computational Chemistry reads as follows: "The committee will investigate and report on opportunities for collaborative and synergistic research in the mathematical sciences t hat can accelerate progress in theoretical and computational chemistry and their applications, and make recommendations for promoting this research." It was clear from the outset that the study could not presume to be exhaustive. However, it seemed real istic to strive for representative sampling of the two communities involved and to identify instructive examples of past collaborative successes, likely prospects for interdisciplinary synergy, and barriers to joint research that could be removed or at le ast lowered.

A number of fruitful collaborations between mathematical scientists and theoretical/computational chemists have occurred in the past. Noteworthy examples include the Nobel prize-winning work of Hauptman and Karle to advance the science of X-ray crystal lography, now a basic tool; quantitative structure-activity relationships have led to the development of at least four commercially successful products (an antibacterial compound, two herbicides, and one fungicide); and insights into molecular structure h ave been gained from mathematical results in group theory and topology.

In scanning the research needs of theoretical/computational chemistry, the committee found opportunities for synergistic research with almost the entire mathematical sciences community, where that term is used in its broadest sense to include core and a pplied mathematicians, statisticians, operations researchers, and theoretical computer scientists. Many of the mathematical lines of research that, if reoriented, could contribute to chemistry are already being pursued in other contexts. The matrix in Figure ES.1 displays a subjective assessment of the depth of potential cross-fertilization between major challenges from theoretical and computational chemistry and relevant topics in the mathematical sciences. This matrix is based to some extent on intui tion because it is an assessment of future research opportunities, not past results. An "H" in the matrix implies an overlap that appears clearly promising, while an "M" suggests that some synergy between the areas is likely. The absence of an H or an M should not be taken to imply that some clever person will not find an application of that technique to that problem at some point.


FIGURE ES.1 Subjective assessment of depth of potential cross-fertilization between major areas of the mathematical sciences and theoretical and computational chemistry. An

H

implies an overlap that is clearly promising and an

M

suggests that some synergy is likely.

Quantum
electronic
structure
Molecular
mechanics
Condensed-
phase
simulations
Density
functionals
N-represent-
ability
Adaptive and Multiscale
methods

H

M

M

   
Special bases

H

 

M

M

 
Differential geometry  

M

     
Functional analysis

H

 

M

H

H

Graph theory

M

M

M

   
Group Theory

H

 

M

 

M

Optimization

H

H

H

H

M

Numerical linear algbra

H

H

 

M

 
Number Theory    

M

   
Pattern Recognition

M

M

H

   
Probability and Statistics

H

 

H

 

M

Several complex variables

H

 

H

M

 
Topology

M

M

H

   
Dynamical systems  

M

H

   
Design
of
molecules
Construc-
tion
of
potential
energy
functions
Gas-
phase
dynamics
Poly-
mers
Topo-
graphy
of
potential
energy
surfaces
Biological
macro-
molecules
(including
protein
folding)
Adaptive and Multiscale
methods
 

M

M

M

M

M

Special bases    

H

     
Differential geometry

M

M

 

M

H

H

Functional analysis  

H

M

 

M

M

Graph theory

H

   

M

M

M

Group Theory

M

 

M

M

 

M

Optimization

H

H

M

H

H

H

Numerical linear algbra  

M

   

M

 
Number Theory    

M

 

M

 
Pattern Recognition

H

 

M

H

H

H

Probability and Statistics

H

 

M

H

H

H

Several complex variables    

M

 

M

M

Topology

H

M

M

H

H

H

Dynamical systems    

H

M

H

M

Conclusions and Recommendations

As a result of its investigations and collective evaluation of the available information, the committee has reached the following conclusions.


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