155 research outputs found
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
A Sparse SCF algorithm and its parallel implementation: Application to DFTB
We present an algorithm and its parallel implementation for solving a self
consistent problem as encountered in Hartree Fock or Density Functional Theory.
The algorithm takes advantage of the sparsity of matrices through the use of
local molecular orbitals. The implementation allows to exploit efficiently
modern symmetric multiprocessing (SMP) computer architectures. As a first
application, the algorithm is used within the density functional based tight
binding method, for which most of the computational time is spent in the linear
algebra routines (diagonalization of the Fock/Kohn-Sham matrix). We show that
with this algorithm (i) single point calculations on very large systems
(millions of atoms) can be performed on large SMP machines (ii) calculations
involving intermediate size systems (1~000--100~000 atoms) are also strongly
accelerated and can run efficiently on standard servers (iii) the error on the
total energy due to the use of a cut-off in the molecular orbital coefficients
can be controlled such that it remains smaller than the SCF convergence
criterion.Comment: 13 pages, 11 figure
Diagrammatic Coupled Cluster Monte Carlo
We propose a modified coupled cluster Monte Carlo algorithm that
stochastically samples connected terms within the truncated
Baker--Campbell--Hausdorff expansion of the similarity transformed Hamiltonian
by construction of coupled cluster diagrams on the fly. Our new approach --
diagCCMC -- allows propagation to be performed using only the connected
components of the similarity-transformed Hamiltonian, greatly reducing the
memory cost associated with the stochastic solution of the coupled cluster
equations. We show that for perfectly local, noninteracting systems, diagCCMC
is able to represent the coupled cluster wavefunction with a memory cost that
scales linearly with system size. The favorable memory cost is observed with
the only assumption of fixed stochastic granularity and is valid for arbitrary
levels of coupled cluster theory. Significant reduction in memory cost is also
shown to smoothly appear with dissociation of a finite chain of helium atoms.
This approach is also shown not to break down in the presence of strong
correlation through the example of a stretched nitrogen molecule. Our novel
methodology moves the theoretical basis of coupled cluster Monte Carlo closer
to deterministic approaches.Comment: 31 pages, 6 figure
Molecular propensity as a driver for explorative reactivity studies
Quantum chemical studies of reactivity involve calculations on a large number
of molecular structures and comparison of their energies. Already the set-up of
these calculations limits the scope of the results that one will obtain,
because several system-specific variables such as the charge and spin need to
be set prior to the calculation. For a reliable exploration of reaction
mechanisms, a considerable number of calculations with varying global
parameters must be taken into account, or important facts about the reactivity
of the system under consideration can go undetected. For example, one could
miss crossings of potential energy surfaces for different spin states or might
not note that a molecule is prone to oxidation. Here, we introduce the concept
of molecular propensity to account for the predisposition of a molecular system
to react across different electronic states in certain nuclear configurations.
Within our real-time quantum chemistry framework, we developed an algorithm
that allows us to be alerted to such a propensity of a system under
consideration.Comment: 10 pages, 7 figure
Speed and accuracy: Having your cake and eating it too
Since the first ab initio methods were developed, the ultimate goal of quantum chemistry has been to provide insights, not readily accessible through experiment, into chemical phenomena. Over the years, two different paths to this end have been taken. The first path provides as accurate a description of relatively small systems as modern computer hardware will allow. The second path follows the desire to perform simulations on systems of physically relevant sizes while sacrificing a certain level of accuracy. The merging of these two paths has allowed for the accurate modeling of large molecular systems through the use of novel theoretical methods. The largest barrier to achieving the goal of accurate calculations on large systems has been the computational requirements of many modern theoretical methods. While these methods are capable of providing the desired level of accuracy, the prohibitive computational requirements can limit system sizes to tens of atoms. By decomposing large chemical systems into more computationally tractable pieces, fragmentation methods have the capability to reduce this barrier and allow for highly accurate descriptions of large molecular systems such as proteins, bulk phase solutions and polymers and nano-scale systems
Fragmentation Methods: A Route to Accurate Calculations on Large Systems
Theoretical chemists have always strived to perform quantum mechanics (QM) calculations on larger and larger molecules and molecular systems, as well as condensed phase species, that are frequently much larger than the current state-of-the-art would suggest is possible. The desire to study species (with acceptable accuracy) that are larger than appears to be feasible has naturally led to the development of novel methods, including semiempirical approaches, reduced scaling methods, and fragmentation methods. The focus of the present review is on fragmentation methods, in which a large molecule or molecular system is made more computationally tractable by explicitly considering only one part (fragment) of the whole in any particular calculation. If one can divide a species of interest into fragments, employ some level of ab initio QM to calculate the wave function, energy, and properties of each fragment, and then combine the results from the fragment calculations to predict the same properties for the whole, the possibility exists that the accuracy of the outcome can approach that which would be obtained from a full (nonfragmented) calculation. It is this goal that drives the development of fragmentation methods
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