# Breakout Group 7: Computation and Informatics

The breakout group on the role of computational and information sciences
on modern catalysis research focused on two key questions:

\(1\) What are the key areas in which advances need to be made in
theory, informatics and simulation to improve our understanding of
catalytic behavior and the control of chemical transformation?

\(2\) (2)

How do we develop a common set of themes, concepts, and language that
enable us to link homogeneous, heterogeneous, and bio-catalysis in order
to improve our control of chemical transformations?

There was general agreement that modern theory, computation, and
informatics are critical to advancing our understanding of catalytic
processes at the molecular level which will lead to improvement in our
capabilities to precisely control chemical transformations.

The key roles of theory and computation are:

> \(1\) Qualitative analyses and results that can help in experimental
> design and the interpretation of experimental results leading to the
> development of new concepts; and
>
> \(2\) Quantitative results that are reliable and accurate enough to
> specifically direct catalysis design at the microscopic or molecular
> level.

The general consensus of the group is that we are well on our way for
role (1) but we are not there yet for role (2). The group then discussed
how to improve role (1) and

how to accomplish role (2). Three areas are critical in the roadmap to
make computational and information science a core part of modern
catalysis research.

\(1\) What is the role of databases, both computational and
experimental?

(

> 2\) How do we benchmark the computational methods against experiment
> in order to validate the different approaches?

(

> 3\) What new computational approaches are needed for computation to
> play a core role in advancing catalysis science?

Finally the group addressed the computational

Finally the g resource requirements need to advance computational
catalysis.

# Databases

The group raised a number of questions as detailed below with some
initial responses.

*What is the role of databases and what goes in them?*

It is certainly possible to define what data should go into a
computational database today but this is a non-trivial task. For
experimental databases, it is far more difficult as we do not even know
what should be in the databases in terms of experimental information.
Although this task will be extremely difficult we cannot wait until it
is easy to do because too much data has been accumulated. This is the
problem that biologists are finding themselves in today -- a flood of
data without the tools to collect into databases, mine it, and analyze
it.

*What should go into a computational database?*

At a minimum, one will require geometry, energy, electron density, and
frequency parameters as well as explicit details of how the calculation
was done. In addition, there are differences between molecular data and
that for the solid state.

*What should go into an experimental database?*

Besides the problem of defining what data should be in the database, it
is unlikely that one database format will be appropriate for all types
of catalytic data. There are similarities and differences between
different types of catalysis data, for example, homogeneous and
heterogeneous catalysis.

*Are there improved ways in which data should be collected in order to
make it easier to manage and analyze?*

On way to do this is to follow the model of the crystallographers, the
Cambridge Data Base for molecular systems and the Protein Database (PDB)
for proteins. The data from crystal structures is submitted with a
manuscript in the proper from for inclusion of one of the crystal
structure databases. There is an important role for peer-reviewed,
archival journals in helping to make sure that key data is provided with
a paper for use in databases. The data may be in the paper or in the
Supplementary Material. By requiring that the data be included before
publication in a usable form provides a great service to the community.
In addition, the development of tools for automated and easy to use data
handling processes need to be developed as well as interfaces for
archival data storage. The community will have to work with the
instrument developers in this area although this is more complicated in
catalysis research because of the wide variety of instrumentation and
the many different types of measurements.

*What is the role of high-throughput technologies in terms of data
management and mining?*

The whole question of the role of high throughput technologies in
catalysis needs to be addressed. If this technology is to play a role in
catalysis research, there are many informatics and data
management/mining issues that need to be addressed.

*What data is important for archival purposes?*

This is of course dependent on the type of study that has been carried
out. Questions include: How do we define elementary step
reactivity/kinetics? How do we define sites at which reactions occur?

*What computer science issues need to be addressed?*

Questions include: What is the best way to represent a chemical reaction
in a data base? How do we define and identify classes of mechanistic
descriptors and predictors? What metadata definitions are best as
metadata describing the data pedigree is critical if we are to be able
to use the data? How was the data generated and how was it analyzed if
summary data are presented? How do we deal with knowledge capture? How
do we deal with storing models and concepts? How do we mine the
literature? How do we mine the data to get the optimal use of it and to
develop new models and concepts? The computer science efforts should
build off developments in bioinformatics and biological data management
which have many of the same problems especially in terms of high
throughput data analysis and management.

Who owns, distributes, and manages the databases and the analysis tools?

Should the databases be owned/managed by a commercial entity or by some
public, open effort? What role would the DOE SciDAC program play in
developing the necessary software tools? Is there a role for Virtual
Catalysis Center in managing the data? There are many models for the
maintenance of the catalysis databases but the group was unanimous in
that this cannot just be left to individual research groups to do their
best.

## Computational Methods Validation and Benchmarking

A critical issue that the group discussed at length was the need for
good experimental data to benchmark and validate computational methods
for catalysis research. A number of issues were raised.

*What is needed to validate models of reactivity? How do we benchmark
our computational methods?*

This is a question with very complex answers but all agreed that it
needed to be addressed. These validations need to be done for both
homogeneous catalysis (molecular level) and heterogeneous catalysis
(solid state). There is no answer today to this question just as noted
above in terms of what should be in a database.

*What data is needed in terms of thermodynamics and kinetics? What
levels of accuracy/reliability are needed?*

For thermodynamic data, bond energies and heats of formation of species
are required. What is the equivalence of the G2 data set so important in
computational chemistry method development for catalysis?) What kinetic
data on specific microscopic, molecular reactions are available? What
microscopic kinetic mechanisms have been validated and what is the
quality of the data? What kinetic data is available for global reactions
under controlled conditions? What data is available for vibrational,
uv-visible, nmr, epr, etc spectroscopies that is relevant to catalytic
activity?

*What do we need to benchmark to get the heart of catalysis --
kinetics?*

Critical information about molecular reactions and processes is needed
from experiment. How do we gain information about the transition state?
Currently, the best methods are to use computational approaches and then
to use kinetic models such as transition state theory to compare rate
constants based on knowledge of the potential energy surface from
electronic structure calculations against experimental rate constants.
This is very successful for gas phase processes but is much harder to do
in solution or on surfaces. Are there experimental ways to get at the
transition state?

*What do we need to know from experiment to benchmark a method? What can
we get from experiment? How similar/different are
homogenoeus/heterogenous catalysis?*

This is very difficult to do for heterogeneous catalysis as we require
information about sites, phases, kinetics of elementary steps,
mechanism, spectroscopy for each elementary step,
thermodynamics/energetics of each step. The possibility exists of using
clusters for some testing and benchmarking but this is not a substitute
for complex surface reactions.

For homogeneous processes, there are some possibilities. For example,
one can study catalytic processes in the gas phase or in supercritical
CO or CO~2~. One could also study a specific reaction in one solvent
where only a ligand on the transition metal center (e.g. Ir-phosphine
complexes) is varied and one would then have a benchmark suite of
relative E~a~'s and thermodynamic values. For Lewis and Bronsted
acid/base catalysis, there is already growing evidence that such
databases can be built for benchmarking computational methods.

### **Software and Theory Needs**

*What improvements in computational methods are needed?*

Density functional theory (DFT) has played a key role in advancing
computational catalysis. Extensive studies have shown that it is
extremely difficult to calculate the geometries and vibrational
frequencies of transition metal compounds at the ab initio molecular
orbital level. In general, one needs to use some type of treatment of
correlation energies, often beyond the second-order Møller-Plesset (MP2)
level. However, DFT calculations at the local and non-local levels can
provide a good description of the geometries and frequencies for a wide
range of transition metal complexes as well as solids. Furthermore, DFT
methods are more computationally efficient than the corresponding ab
initio molecular orbital methods with the inclusion of electron
correlation. An issue with DFT is that we do not yet have
exchange-correlation functionals that are accurate or reliable enough
yet to be used true first principles predictions of catalytic behavior.
The true computational design of practical catalysts for industrial and
commercial applications will require the ability to predict accurate
thermodynamic and kinetic results as a factor of 2 to 4 in catalyst
efficiency can determine the economic feasibility of a process. An error
of only 1.4 kcal/mol in reaction energies leads to an error in
predicting an equilibrium constant of a factor of 10 and the same error
in the activation energy leads to an error of a factor of 10 in a rate
constant, both at room temperature. The requirement for such accuracy
means that we must be able to predict thermodynamic and kinetic
quantities to very high accuracy, on the order of tenths of a kcal/mol
for thermodynamic properties and reaction rates to 25% -- a daunting
computational task. However, current ab initio methods are often too
computationally costly and current DFT exchange-correlation functionals
are not accurate enough. An example of the latter is the difficulty in
predicting the barrier heights for radical abstraction reactions. A
critical issue, therefore, is how do we go beyond DFT or how do we fix
DFT to provide an accurate quantum chemical description of active site?
New functionals, especially for correlation, are needed as it is
understood today how to fix issues with exchange functionals. An example
of where improved functionals are needed is the ability to predict band
gaps in solids. In addition, one needs to improve the ability of current
functionals to deal with weak interactions such as van der Waals
interactions or hydrogen bonds. All of these advances need to be made
with retention of the speed of current DFT approaches so that the
scaling with system size does not make the computations intractable. In
addition, we need to continue to improve the computational scaling and
efficiency of accurate molecular orbital methods such as coupled cluster
(CCSD(T)) and multireference methods (MRCI).

The group noted that benchmarks on the accuracy of DFT methods are
needed especially for the experimental user community

We need to improve methods for finding transition states for complex
systems with many degrees of freedom, especially for solids. Are current
algorithms for finding transitions states as computationally efficient
as they could be? Methods for dealing with calculating vibrational
frequencies (second derivatives) in constrained systems need to be
developed to improve transitions state optimizations.

*How do we treat the environment -- solution, interfaces, solids? How do
we deal with the nanoscale?*

We need to improve the capabilities and ease of use of hierarchical
methods such as the ONIOM method with accurate methods for active sites
and lower level methods for the surroundings. Other electronic structure
methods requiring improvements include embedded cluster approaches,
tight binding methods such as the recently developed SCC-DFT-TB, and
semi-empirical MO/DFT methods with improved parameters. Improved
molecular mechanics approaches including the ability to model transition
states would be extremely valuable. Of course, transferable parameters
for all empirical-based methods are required.

*What are the appropriate methods for predicting kinetics accurately?*

Currently most tractable computational methods are based on some
variation of transition-state theory (TST). Improvements include
variational TST (VTST) and inclusion of quantum dynamical features such
as tunneling. What other methods for dealing with the nuclear motion
problem are appropriate for use in catalysis? This deals not only with
reactions but also vibrational treatments including the effects of
anharmonicity. What is the role of molecular dynamics studies in
studying complex chemical reactions in solution and on surfaces? What
are appropriate methods for treating surface diffusion and other
dynamical features? What is the role of Monte Carlo methods?

*How do we deal with the different temporal and spatial scales relevant
to catalysis?*

This is a complex issue that will need to be addressed.

*What are critical requirements for software for this community?*

The software should be easy to use for general users yet include
capabilities required by expert users. Examples of such software include
Ecce and GaussView. It is important to be able to have software tools to
integrate across methods, an example being the Khimera software which
takes output from electronic structure codes such as Gaussian and
enables rate constant and model kinetic calculations. We must provide
easy access to codes especially for solid state calculations and improve
current tools for setting up and running calculations on the solid
state. We need to provide links to experimental and computational
databases to provide information on the quality of the computational
results.

**Hardware and computational grand challenges**

In order to make computation a true partner with experiment for
catalysis research, access for the catalysis community to integrated
hardware & software systems must be provided. This could be done through
a Virtual Catalysis Center. In addition, the computational catalysis
community needs to pick a Grand Challenge Computational Problem for the
next NERSC INCITE call or for calls to use the new ORNL Cray machine for
a large block of resource allocation and use the resource to solve a
significant problem in catalysis science. This would raise awareness of
the importance of computing in catalysis and the role of large machines
in addressing national scientific and technical needs such as catalysis.
Much of the computational catalysis community already has access to the
11.5 TFlop Linux cluster in the MSCF in the EMSL at PNNL.

Given the advances in methods and software described above, the
fundamental science goals that could be accomplished on next and future
generation computer architectures in computational catalysis are:

**50 TFlops**: accurate calculations for realistic, isolated homogeneous
catalyst model systems (\<1.0 kcal/mol thermodynamics, \<50% error in
reaction rates).

**250 TFlops**: accurate calculations for realistic homogeneous catalyst
model systems in solution and heterogeneous catalysts in vacuum (\<1.0
kcal/mol thermodynamics, \<50% error in reaction rates).

**1000 Tflops**: accurate calculations for realistic homogeneous
catalyst model systems in solution and heterogeneous catalysts in
solution (\<1.0 kcal/mol thermodynamics, \<50% error in reaction rates).