Constantinos C. Pantelides

Towards crystal structure prediction of complex organic compounds – a report on the fifth blind test

The results of the fifth blind test of crystal structure prediction, which show important success with more challenging large and flexible molecules, are presented and discussed.

Monte Carlo evaluation of derivative-based global sensitivity measures

Abstract A novel approach for evaluation of derivative-based global sensitivity measures (DGSM) is presented. It is compared with the Morris and the Sobol’ sensitivity indices methods. It is shown that there is a link between DGSM and Sobol’ sensitivity indices. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is many orders of magnitude lower than that for estimation of the Sobol’ sensitivity indices. It is also lower than that for the Morris method. Efficiencies of Monte Carlo (MC) and quasi-Monte Carlo (QMC) sampling methods for calculation of DGSM are compared. It is shown that the superiority of QMC over MC depends on the problems effective dimension, which can also be estimated using DGSM.

Report on the sixth blind test of organic crystal structure prediction methods

The results of the sixth blind test of organic crystal structure prediction methods are presented and discussed, highlighting progress for salts, hydrates and bulky flexible molecules, as well as on-going challenges.

Successful prediction of a model pharmaceutical in the fifth blind test of crystal structure prediction

The range of target structures in the fifth international blind test of crystal structure prediction was extended to include a highly flexible molecule, (benzyl-(4-(4-methyl-5-(p-tolylsulfonyl)-1,3-thiazol-2-yl)phenyl)carbamate, as a challenge representative of modern pharmaceuticals. Two of the groups participating in the blind test independently predicted the correct structure. The methods they used are described and contrasted, and the implications of the capability to tackle molecules of this complexity are discussed.

Efficient Handling of Molecular Flexibility in Lattice Energy Minimization of Organic Crystals

This paper presents a novel algorithm, CrystalOptimizer, for the minimization of the lattice energy of crystals formed by flexible molecules. The algorithm employs isolated-molecule quantum mechanical (QM) calculations of the intramolecular energy and conformation-dependent atomic multipoles in the course of the lattice energy minimization. The algorithm eliminates the need to perform QM calculations at each iteration of the minimization by using Local Approximate Models (LAMs), with a minimal impact on accuracy. Additional computational efficiencies are achieved by storing QM-derived components of the lattice energy model in a database and reusing them in subsequent calculations whenever possible. This makes the approach particularly well suited to applications that involve a sequence of lattice energy evaluations, such as crystal structure prediction. The algorithm is capable of handling efficiently complex systems with considerable conformational flexibility. The paper presents examples of the algorithms application ranging from single-component crystals to cocrystals and salts of flexible molecules with tens of intramolecular degrees of freedom whose optimal values are determined by the interplay of conformational strain and packing forces. For any given molecule, the degree of flexibility to be considered can vary from a few torsional angles to relaxation of the entire set of torsion angles, bond angles, and bond lengths present in the molecule.

The online use of first-principles models in process operations: Review, current status and future needs

Abstract The online use of first-principles models (FPMs) to support process operations has been practised in the chemical and petroleum industry for over 40 years. FPMs can encapsulate a large amount of process knowledge and many companies have realized significant value from the use of these models in online model based applications (OMBAs). Such applications include real-time optimization, model predictive control, data reconciliation, virtual sensors, and process performance monitoring to name a few. The sophistication of both the FPM models and applications based on them has increased over time. At some points in the evolution certain applications were not successful due to issues related to sustainability, which includes model complexity, solvability, maintainability and tractability. Also, model development cost can be a factor in considering the type of model used in these applications. Hence many simplified and empirical model-based online applications became preferred in some domains, even though the overall prediction quality of the FPM may be superior. This paper will review the past experiences, current status and future challenges related to FPM based online modeling applications. There are many areas where the issues related to FPMs can be addressed through proper model management, better software tools and improved technical approaches and work processes. It is hoped that this paper can serve as a basis to promote an understanding of the issues for researchers, modeling software vendors, modeling engineers, and application engineers and help to stimulate improvements in this area leading to increased usage and value of FPMs in supporting process operations.

The polymorphs of ROY: application of a systematic crystal structure prediction technique

We investigate the ability of current ab initio crystal structure prediction techniques to identify the polymorphs of 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile, also known as ROY because of the red, orange and yellow colours of its polymorphs. We use a methodology combining the generation of a large number of structures based on a computationally inexpensive model using the CrystalPredictor global search algorithm, and the further minimization of the most promising of these structures using the CrystalOptimizer local minimization algorithm which employs an accurate, yet efficiently constructed, model based on isolated-molecule quantum-mechanical calculations. We demonstrate that this approach successfully predicts the seven experimentally resolved structures of ROY as lattice-energy minima, with five of these structures being within the 12 lowest energy structures predicted. Some of the other low-energy structures identified are likely candidates for the still unresolved polymorphs of this molecule. The relative stability of the predicted structures only partially matches that of the experimentally resolved polymorphs. The worst case is that of polymorph ON, whose relative energy with respect to Y is overestimated by 6.65 kJ mol(-1). This highlights the need for further developments in the accuracy of the energy calculations.

Efficient handling of molecular flexibility in ab initio generation of crystal structures

A key step in many approaches to crystal structure prediction (CSP) is the initial generation of large numbers of candidate crystal structures via the exploration of the lattice energy surface. By using a relatively simple lattice energy approximation, this global search step aims to identify, in a computationally tractable manner, a limited number of likely candidate structures for further refinement using more detailed models. This paper presents an effective and efficient approach to modeling the effects of molecular flexibility during this initial global search. Local approximate models (LAMs), constructed via quantum mechanical (QM) calculations, are used to model the conformational energy, molecular geometry, and atomic charge distributions as functions of a subset of the conformational degrees of freedom (e.g., flexible torsion angles). The effectiveness of the new algorithm is demonstrated via its application to the recently studied 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY) molecule and to two molecules, β-D-glucose and 1-(4-benzoylpiperazin-1-yl)-2-(4,7-dimethoxy-1H-pyrrolo[2,3-c]pyridin-3-yl)ethane-1,2-dione, a Bristol Myers Squibb molecule referenced as BMS-488043. All three molecules present significant challenges due to their high degree of flexibility.

General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules

The prediction of the possible crystal structure(s) of organic molecules is an important activity for the pharmaceutical and agrochemical industries, among others, due to the prevalence of crystalline products. This chapter considers the general requirements that crystal structure prediction (CSP) methodologies need to fulfil in order to be able to achieve reliable predictions over a wide range of organic systems. It also reviews the current status of a multistage CSP methodology that has recently proved successful for a number of systems of practical interest. Emphasis is placed on recent developments that allow a reconciliation of conflicting needs for, on the one hand, accurate evaluation of the energy of a proposed crystal structure and on the other hand, comprehensive search of the energy landscape for the reliable identification of all low-energy minima. Finally, based on the experience gained from this work, current limitations and opportunities for further research in this area are identified. We also consider issues relating to the use of empirical models derived from experimental data in conjunction with ab initio CSP.

Optimal control of systems described by index two differential-algebraic equations

The optimal control problems considered here seek to determine a time-varying control action and a set of time-invariant parameters that optimize the performance of a dynamic system whose behaviour is described by index two differential-algebraic equations (DAEs).The problem formulation accommodates equality and inequality end and interior point constraints as well as constraints on control variables and parameters. The control parameterization approach, whereby the original problem is transformed into a nonlinear programming problem, is adopted.Due to the features of index two DAEs, the control representation employed may yield a discontinuous system trajectory and for this reason it is necessary to define functions yielding consistent initial conditions following control variable discontinuities. Variational analysis is carried out to derive expressions for the objective and constraint function gradients with respect to the optimization decision variables. A key characteristic of this analysis is that, ...