Victor E. Bazterra

Significant progress in predicting the crystal structures of small organic molecules – a report on the fourth blind test

We report on the organization and outcome of the fourth blind test of crystal structure prediction, an international collaborative project organized to evaluate the present state in computational methods of predicting the crystal structures of small organic molecules. There were 14 research groups which took part, using a variety of methods to generate and rank the most likely crystal structures for four target systems: three single-component crystal structures and a 1:1 cocrystal. Participants were challenged to predict the crystal structures of the four systems, given only their molecular diagrams, while the recently determined but as-yet unpublished crystal structures were withheld by an independent referee. Three predictions were allowed for each system. The results demonstrate a dramatic improvement in rates of success over previous blind tests; in total, there were 13 successful predictions and, for each of the four targets, at least two groups correctly predicted the observed crystal structure. The successes include one participating group who correctly predicted all four crystal structures as their first ranked choice, albeit at a considerable computational expense. The results reflect important improvements in modelling methods and suggest that, at least for the small and fairly rigid types of molecules included in this blind test, such calculations can be constructively applied to help understand crystallization and polymorphism of organic molecules.

A third blind test of crystal structure prediction

Following the interest generated by two previous blind tests of crystal structure prediction (CSP1999 and CSP2001), a third such collaborative project (CSP2004) was hosted by the Cambridge Crystallographic Data Centre. A range of methodologies used in searching for and ranking the likelihood of predicted crystal structures is represented amongst the 18 participating research groups, although most are based on the global minimization of the lattice energy. Initially the participants were given molecular diagrams of three molecules and asked to submit three predictions for the most likely crystal structure of each. Unlike earlier blind tests, no restriction was placed on the possible space group of the target crystal structures. Furthermore, Z = 2 structures were allowed. Part-way through the test, a partial structure report was discovered for one of the molecules, which could no longer be considered a blind test. Hence, a second molecule from the same category (small, rigid with common atom types) was offered to the participants as a replacement. Success rates within the three submitted predictions were lower than in the previous tests - there was only one successful prediction for any of the three ;blind molecules. For the ;simplest rigid molecule, this lack of success is partly due to the observed structure crystallizing with two molecules in the asymmetric unit. As in the 2001 blind test, there was no success in predicting the structure of the flexible molecule. The results highlight the necessity for better energy models, capable of simultaneously describing conformational and packing energies with high accuracy. There is also a need for improvements in search procedures for crystals with more than one independent molecule, as well as for molecules with conformational flexibility. These are necessary requirements for the prediction of possible thermodynamically favoured polymorphs. Which of these are actually realised is also influenced by as yet insufficiently understood processes of nucleation and crystal growth.

Modified genetic algorithm to model crystal structures. I. Benzene, naphthalene and anthracene

This paper describes a new computational scheme to model crystal structures of organic compounds employing a modified genetic algorithm. The method uses real-valued Cartesian coordinates and Euler angles between molecules in a crystal block as variables identifying the genetic parameters, i.e., genes. The model does not make any assumption on the crystallographic group at which the compound belongs nor to the number of molecules in the unit cell. The method has been implemented in the computer package MGAC (Modified Genetic Algorithm for Crystal and Cluster structures) that allows for optimizations using any arbitrary selection function. The examples presented here for the crystalline structures of benzene, naphthalene and anthracene, using an empirical potential energy function as the selection function, show excellent agreement with the experimental ones. While these examples use the “rigid molecule approximation,” the method is quite general and can be extended to take into account any number of intram...

On the theoretical determination of the static dipole polarizability of intermediate size silicon clusters

The B3PW91 method of the density functional theory has been applied to the study of the dipole polarizability of medium size silicon clusters employing pseudopotential on all of them. All electron calculations have been performed for those clusters with less than nine atoms. In addition, we have optimized the structures of the clusters with less than ten atoms. On using the modified genetic algorithm, fourteen conformers of silicon isomers with nine atoms have been determined. The corresponding geometry of these clusters was optimized and their relative stability determined. The calculated polarizabilities are compared with experimental data and previous theoretical results.

A general framework to understand parallel performance in heterogeneous clusters: analysis of a new adaptive parallel genetic algorithm

This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors.

Modified genetic algorithm to model crystal structures. II. Determination of a polymorphic structure of benzene using enthalpy minimization

The Modified Genetic Algorithm scheme, presented in Paper I to model crystal structures in organic compounds (MGAC), is applied here to test its performance in the determination of a polymorphic crystalline structure of benzene that has been observed at 25 Kbar. This polymorph, named here benzene II, is not a global minimum of the energy or even close to any of the local minimum determined using the energy evolution of benzene structures described in the previous paper. The benzene II structure corresponds to an enthalpy minimum. This paper shows that it is possible to use the MGAC procedure, modified to use the minimization of the enthalpy instead of the energy in the GA (Genetic Algorithm) selection process, to find this high pressure structure of benzene.

Theoretical study of the adsorption of H on Sin clusters, (n=3–10)

A recently proposed local Fukui function is used to predict the binding site of atomic hydrogen on silicon clusters. To validate the predictions, an extensive search for the more stable SinH (n=3-10) clusters has been done using a modified genetic algorithm. In all cases, the isomer predicted by the Fukui function is found by the search, but it is not always the most stable one. It is discussed that in the cases where the geometrical structure of the bare silicon cluster suffers a considerable change due to the addition of one hydrogen atom, the situation is more complicated and the relaxation effects should be considered.

A Distributed Computing Method for Crystal Structure Prediction of Flexible Molecules: An Application to N-(2-Dimethyl-4,5-dinitrophenyl) Acetamide.

In this paper, we describe a new distributed computing framework for crystal structure prediction that is capable of performing crystal structure searches for flexible molecules within any space group and with an arbitrary number of molecules in the asymmetric unit. The distributed computing framework includes a series of tightly integrated computer programs for generating the molecules force field, sampling possible crystal structures using a distributed parallel genetic algorithm, locally minimizing these structures and classifying, sorting, and archiving the most relevant ones. As an example, we report the results of its application to the prediction of the crystal structure of the elusive N-(2-dimethyl-4,5-dinitrophenyl) acetamide, a molecule for which its crystal structure proved to be one of the most difficult cases in the last CSP2004 blind test for crystal structure prediction.

Global Optimization of Atomic Cluster Structures Using Parallel Genetic Algorithms

The study of the structure and physical properties of atomic clusters is an extremely active area of research due to their importance, both in fundamental science and in applied technology. For medium size atomic clusters most of the structures reported today have been obtained by local optimizations of plausible structures using DFT (Density Functional Theory) methods and/or by global optimizations in which much more approximate methods are used to calculate the cluster’s energetics. Our previous work shows that these approaches can not be reliably used to study atomic cluster structures and that approaches based on global optimization schemes are needed. In this paper, we report the implementation and application of a parallel Genetic Algorithm (GA) to predict the structure of medium size atomic clusters.

Digital Sherpa: a set of high level tools to manage scientific applications in a computational grid

Currently users of high performance computers are overwhelmed with non scalable tasks such as job submission and monitoring, a problem that gets compounded when trying to run complex scientific applications requiring the coordination of several interrelated programs. Digital Sherpa (DS) is a grid tool set for coordinating the execution of multiple jobs on separate HPC resources; DS automates non-scalable tasks such as job submission and monitoring, and includes recovery features such as resubmission of failed jobs and program restarting. DS has been used to develop a Grid enabled version, MGAC-CGA, of the Modified Genetic Algorithms for Crystals and Clusters (MGAC), a parallel distributed application for the prediction of the structures of atomic clusters and organic crystals using Genetic Algorithms (GA). MGAC-CGA has been successfully tested on the NSF TeraGrid and on several clusters at the University of Utah.