168 results found
Bowskill DH, Sugden IJ, George N, et al., 2020, Efficient Parameterization of a Surrogate Model of Molecular Interactions in Crystals, Computer Aided Chemical Engineering, Pages: 493-498
© 2020 Elsevier B.V. We propose a surrogate model for lattice energy that allows the accurate prediction of the crystal structures formed by a given molecule and their relative stability ranking. The model is derived from a combination of isolated-molecule quantum mechanical calculations and a relatively small number of more expensive solid-state DFT-D computations. The surrogate model provides an effective mechanism for refining the crystal structure landscape predicted by current Crystal Structure Prediction methodologies. Applied to the agrochemical Chlorothalonil, the approach is shown to be highly accurate whilst reducing the computational costs by approximately a factor of 20 compared to refinement of all structures using solid-state DFT.
Kusumo KP, Gomoescu L, Paulen R, et al., 2020, Nested Sampling Strategy for Bayesian Design Space Characterization, Computer Aided Chemical Engineering, Pages: 1957-1962
© 2020 Elsevier B.V. Design space is a key concept in pharmaceutical quality by design, providing better understanding of manufacturing processes and enhancing regulatory flexibility. It is of paramount importance to develop computational techniques for providing quantitative representations of a design space, in accordance with the ICH Q8 guideline. The focus is on Bayesian approaches to design space characterization, which rely on a process model to determine a feasibility probability that is used for measuring reliability and risk. The paper presents three improvements over an existing nested sampling method: two-phase strategy with the first phase using a cheap sorting function based on nominal model parameters; dynamic sampling strategy to refine the target design space; and vectorization to evaluate costly functions in parallel. These improvements are implemented as part of the python package DEUS and demonstrated on an industrial case study.
Kusumo KP, Gomoescu L, Paulen R, et al., 2019, Bayesian approach to probabilistic design space characterization: a nested sampling strategy, Industrial & Engineering Chemistry Research, Vol: 59, Pages: 2396-2408, ISSN: 0888-5885
Quality by design in pharmaceutical manufacturing hinges on computational methods and tools that are capable of accurate quantitative prediction of the design space. This paper investigates Bayesian approaches to design space characterization, which determine a feasibility probability that can be used as a measure of reliability and risk by the practitioner. An adaptation of nested sampling—a Monte Carlo technique introduced to compute Bayesian evidence—is presented. The nested sampling algorithm maintains a given set of live points through regions with increasing probability feasibility until reaching a desired reliability level. It furthermore leverages efficient strategies from Bayesian statistics for generating replacement proposals during the search. Features and advantages of this algorithm are demonstrated by means of a simple numerical example and two industrial case studies. It is shown that nested sampling can outperform conventional Monte Carlo sampling and be competitive with flexibility-based optimization techniques in low-dimensional design space problems. Practical aspects of exploiting the sampled design space to reconstruct a feasibility probability map using machine learning techniques are also discussed and illustrated. Finally, the effectiveness of nested sampling is demonstrated on a higher-dimensional problem, in the presence of a complex dynamic model and significant model uncertainty.
de Prada C, Pantelides CC, Luis Pitarch J, 2019, Special Issue on "Process Modelling and Simulation", PROCESSES, Vol: 7
Sugden IJ, Adjiman C, Pantelides C, 2019, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. II. Smoothed intramolecular potentials, Acta Crystallographica Section B: Structural Science, Vol: 75, Pages: 423-433, ISSN: 0108-7681
The application of Crystal Structure Prediction (CSP) to industrially-relevant molecules requires the handling of increasingly large and flexible compounds. We present a revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, we show that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z’=1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.
Bernardi A, Gomoescu L, Wang J, et al., 2019, Kinetic Model Discrimination for Methanol and DME Synthesis using Bayesian Estimation, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER SCIENCE BV, Pages: 335-340, ISSN: 2405-8963
Adjiman CSJ, Pantelides C, Gatsiou CA, 2018, Repulsion-dispersion parameters for the modelling of organic molecular crystals containing N, O, S and Cl, Faraday Discussions, Vol: 211, Pages: 297-323, ISSN: 1359-6640
In lattice energy models that combine ab initio and empirical components, it is important to ensureconsistency between these components so that meaningful quantitative results are obtained. Amethod for deriving parameters of atom-atom repulsion dispersion potentials for crystals, tailoredto different ab initio models is presented. It is based on minimization of the sum of squared de-viations between experimental and calculated structures and energies. The solution algorithmis designed to avoid convergence to local minima in the parameter space by combining a deter-ministic low-discrepancy sequence for the generation of multiple initial parameter guesses withan efficient local minimization algorithm. The proposed approach is applied to derive transferableexp-6 potential parameters suitable for use in conjunction with a distributed multipole electrostaticsmodel derived from isolated molecule charge densities calculated at the M06/6-31G(d,p) level oftheory. Data for hydrocarbons, azahydrocarbons, oxohydrocarbons, organosulphur compoundsand chlorohydrocarbons are used for the estimation. A good fit is achieved for the new set ofparameters with a mean absolute error in sublimation enthalpies of 4.1 kJ/mol and an averagermsd15of 0.31 Å. The parameters are found to perform well on a separate cross-validation set of39 compounds.
Pitt JA, Gomoescu L, Pantelides CC, et al., 2018, Critical assessment of parameter estimation methods in models of biological oscillators, IFAC-PapersOnLine, Vol: 51, Pages: 72-75, ISSN: 2405-8963
Many biological systems exhibit oscillations in relation to key physiological or cellular functions, such as circadian rhythms, mitosis and DNA synthesis. Mathematical modelling provides a powerful approach to analysing these biosystems. Applying parameter estimation methods to calibrate these models can prove a very challenging task in practice, due to the presence of local solutions, lack of identifiability, and risk of overfitting. This paper presents a comparison of three state-of-the-art methods: frequentist, Bayesian and set-membership estimation. We use the Fitzhugh-Nagumo model with synthetic data as a case study. The computational performance and robustness of these methods is discussed, with a particular focus on their predictive capability using cross-validation.
Bowskill D, Sugden I, Gatsiou C-A, et al., 2018, New potentials for accurate and efficient ab initio crystal structure prediction methods, Publisher: INT UNION CRYSTALLOGRAPHY, Pages: E362-E362, ISSN: 2053-2733
Lafitte T, Papaioannou V, Dufal S, et al., 2017, gSAFT: Advanced physical property prediction for process modelling, 27th European Symposium on Computer-Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1003-1008, ISSN: 1570-7946
Sugden IJ, Adjiman CSA, Pantelides C, 2016, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models, Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, Vol: 72, Pages: 864-874, ISSN: 2052-5206
The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015). J Chem Theory Comput 11, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly et al., (2016). Acta Cryst. B, 72, 439-459), which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.
Doherty MF, Grossmann IE, Pantelides CC, 2016, A tribute to professor Roger Sargent: Intellectual leader of process systems engineering, AIChE Journal, Vol: 62, Pages: 2951-2958, ISSN: 0001-1541
This article and this issue of the AIChE Journal, is a tribute to Professor Roger Sargent who, as pioneer and intellectual leader of process systems engineering, has had a profound impact on the discipline of chemical engineering. Spanning more than five decades, his work has provided a strong mathematical foundation to process systems engineering through the development of sophisticated mathematical and computational tools for the simulation, design, control, operation and optimization of chemical processes. In this article we first give a brief overview of his career that included several leadership positions and the establishment of the Centre for Process Systems Engineering (CPSE) at Imperial College London. We next review his research contributions in the areas of process modeling, differential algebraic systems, process dynamics and control, nonlinear optimization and optimal control, design under uncertainty, and process scheduling. We highlight the tremendous impact that he has had through his students, students' students, and his entire academic family tree, which at present contains over 2000 names, probably one of the largest among the academic leaders of chemical engineering. Finally, we provide a brief overview of him as a modest and charming individual with a wonderful sense of humor. He is without doubt a true intellectual giant who has helped to expand the scope of chemical engineering by providing a strong systems component to it, and by establishing strong multidisciplinary links with other fields. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2951–2958, 2016.
Reilly AM, Cooper RI, Adjiman CS, et al., 2016, Report on the sixth blind test of organic crystal structure prediction methods., Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, Vol: 72, Pages: 439-459, ISSN: 2052-5206
The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.
García Muñoz S, Yu W, Pantelides CC, 2016, SimCU: A new model to assess content uniformity of oral dosages based on particulate mass balances and Monte Carlo simulations, Chemical Engineering Research and Design, Vol: 109, Pages: 532-539, ISSN: 0263-8762
A new model (SimCU) is presented to assess the risk of failure on content uniformity for oral dosage forms. The proposed model is an extension to the algorithm presented by Zhang and Johnson (Int. J. Pharm. 1997;154:179-183), including a revision needed to prevent artificial loss of drug and segregation. Furthermore, the improved approach is extended to enable the user to consider alternate sources of variability such as the weight distribution of the dosage forms and the effect of a drug product intermediate (i.e., a granule) with heterogeneous potency levels across particle sizes. The particle mass balance in the corrected algorithm is consistent and its closed form solution for the prediction of the relative standard deviation of potency is presented herein. Predictions of the relative standard deviation (RSD) from SimCU were extensively verified with data from development, clinical and commercial manufacture. Such results support the usage of the proposed model to assess the risk of failure for content uniformity for oral dosage forms which will ultimately drive the specifications for particle size distribution of the drug.
Habgood M, Sugden IJ, Kazantsev AV, et al., 2015, Efficient Handling of Molecular Flexibility in Ab Initio Generation of Crystal Structures, Journal of Chemical Theory and Computation, Vol: 11, Pages: 1957-1969, ISSN: 1549-9626
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.
Vasileiadis M, Pantelides CC, Adjiman CS, 2014, Prediction of the crystal structures of axitinib, a polymorphic pharmaceutical molecule, Chemical Engineering Science, Vol: 121, Pages: 60-76, ISSN: 0009-2509
Organic molecules can crystallize in multiple structures or polymorphs, yielding crystals with very different physical and mechanical properties. The prediction of the polymorphs that may appear in nature is a challenge with great potential benefits for the development of new products and processes. A multistage crystal structure prediction (CSP) methodology is applied to axitinib, a pharmaceutical molecule with significant polymorphism arising from molecular flexibility. The CSP study is focused on those polymorphs with one molecule in the asymmetric unit. The approach successfully identifies all four known polymorphs within this class, as well as a large number of other low-energy structures. The important role of conformational flexibility is highlighted. The performance of the approach is discussed in terms of both the quality of the results and various algorithmic and computational aspects, and some key priorities for further work in this area are identified.
Kazantsev AV, Karamertzanis PG, Pantelides CC, et al., 2014, CrystalOptimizer: An Efficient Algorithm for Lattice Energy Minimization of Organic Crystals Using Isolated-Molecule Quantum Mechanical Calculations, Process Systems Engineering, Pages: 1-42, ISBN: 9783527316847
Pantelides CC, Adjiman CS, Kazantsev AV, 2014, General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules, PREDICTION AND CALCULATION OF CRYSTAL STRUCTURES: METHODS AND APPLICATIONS, Vol: 345, Pages: 25-58, ISSN: 0340-1022
Rodriguez J, Andrade A, Lawal A, et al., 2014, An integrated framework for the dynamic modelling of solvent-based CO2 capture processes, 12th International Conference on Greenhouse Gas Control Technologies (GHGT), Publisher: ELSEVIER SCIENCE BV, Pages: 1206-1217, ISSN: 1876-6102
Pantelides CC, Renfro JG, 2013, THE ONLINE USE OF FIRST-PRINCIPLES MODELS IN PROCESS OPERATIONS: REVIEW, CURRENT STATUS & FUTURE NEEDS
Vasileiadis M, Adjiman CS, Pantelides CC, 2013, Ab initio prediction of crystal structure and the effects of temperature on the relative stability of enantiotropic polymorphs, Pages: 460-461
Vasileiadis M, Kazantsev AV, Karamertzanis PG, et al., 2012, The polymorphs of ROY: application of a systematic crystal structure prediction technique, ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS, Vol: 68, Pages: 677-685
Avaullee L, Adjiman CS, Calado F, et al., 2012, Gsaft: Application of the SAFT-γ mie group contribution EoS in the Oil/Gas Industry - From academic research to industrial deployment, AIChE 2012 - 2012 AIChE Annual Meeting, Conference Proceedings
SAFT-γ Mie is a new equation of state recently developed by the Molecular Systems Engineering group at Imperial College London. It is an advanced group-contribution form of the SAFT equation of state making use of the Mie potential for a more accurate and flexible description of the dispersive/repulsive interactions between segments. One of its key characteristics is the accurate description of vapour/liquid phase equilibria, including the region of the critical point, as well as the second-derivative thermodynamic properties such as the thermal expansivity, isothermal compressibility, heat capacity, Joule-Thomson coefficient, and speed of sound. In 2009, Process Systems Enterprise (PSE) acquired the exclusive intellectual property rights associated with SAFT-γ Mie and related work, for the purpose of incorporating these developments within its gSAFT advanced thermodynamics technology for process modelling. In late 2010, TOTAL, PSE and Imperial College embarked on a joint project aimed at exploring in detail the applicability, benefits and limitations of this technology on a wide range of mixtures of interest to the oil & gas industry. The current phase of the project is primarily focused on mixtures of hydrocarbons (alkanes and aromatics), carbon dioxide, water and methanol. The main output is a single, consistent set of group parameters capable of accurately describing the behaviour of these generic mixtures within the SAFT-γ Mie framework. Starting with a brief overview of the SAFT-γ Mie equation of state, this paper primarily focuses on the systematic methodology employed in developing the corresponding like and unlike group parameters. This comprises a sequence of steps including the choice of representative components and mixtures, the definition of an appropriate set of groups required to describe them, the collection of the necessary experimental data, a streamlined set of software tools and workflows employed for the accurate, ef
Bardwell DA, Adjiman CS, Arnautova YA, et al., 2011, Towards crystal structure prediction of complex organic compounds - a report on the fifth blind test, ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS, Vol: 67, Pages: 535-551
Kazantsev AV, Karamertzanis PG, Adjiman CS, et al., 2011, Successful prediction of a model pharmaceutical in the fifth blind test of crystal structure prediction, INTERNATIONAL JOURNAL OF PHARMACEUTICS, Vol: 418, Pages: 168-178, ISSN: 0378-5173
Kazantsev AV, Karamertzanis PG, Pantelides CC, et al., 2011, CrystalOptimizer: An Efficient Algorithm for Lattice Energy Minimization of Organic Crystals Using Isolated-Molecule Quantum Mechanical Calculations, Process Systems Engineering, Pages: 1-42, ISBN: 9783527316953
Kazantsev AV, Karamertzanis PG, Adjiman CS, et al., 2011, Efficient Handling of Molecular Flexibility in Lattice Energy Minimization of Organic Crystals, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, Vol: 7, Pages: 1998-2016, ISSN: 1549-9618
Vasileiadis M, Kazantsev AV, Karamertzanis PG, et al., 2011, The polymorphs of ROY: Application of crystal structure prediction techniques, Pages: 856-857
Kazantsev AV, Karamertzanis PG, Pantelides CC, et al., 2010, Ab Initio Crystal Structure Prediction for Flexible Molecules, 20th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 817-822, ISSN: 1570-7946
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