Enhancing Entropy and Enthalpy Fluctuations to Drive Crystallization in Atomistic Simulations

Piaggi, Pablo M.; Valsson, Ómar; Parrinello, Michele

doi:10.1103/physrevlett.119.015701

Cited by 97 publications

(105 citation statements)

References 32 publications

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“…For a discussion of S 2 we refer the reader to Ref. 11. This has been a simple proof of principle to show that crystal structures, even the ones that are stabilized by entropy 12 , can be predicted.…”

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confidence: 95%

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Predicting polymorphism in molecular crystals using orientational entropy

Piaggi

Parrinello

2018

Proc. Natl. Acad. Sci. U.S.A.

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107

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We introduce a computational method to discover polymorphs in molecular crystals at finite temperature. The method is based on reproducing the crystallization process starting from the liquid and letting the system discover the relevant polymorphs. This idea, however, conflicts with the fact that crystallization has a time scale much longer than that of molecular simulations. In order to bring the process within affordable simulation time, we enhance the fluctuations of a collective variable by constructing a bias potential with well tempered metadynamics. We use as collective variable an entropy surrogate based on an extended pair correlation function that includes the correlation between the orientation of pairs of molecules. We also propose a similarity metric between configurations based on the extended pair correlation function and a generalized Kullback-Leibler divergence. In this way, we automatically classify the configurations as belonging to a given polymorph using our metric and a hierarchical clustering algorithm. We find all relevant polymorphs for both substances and we predict new polymorphs. One of them is stabilized at finite temperature by entropic effects.Polymorphism is the ability that substances have to crystallize into different structures. A paradigmatic example is carbon that in its two main polymorphs, graphite and diamond, exhibits amazingly different properties. Polymorphism is also important from a practical point of view since controlling which crystal structure forms is of the utmost importance in many manufacturing processes. The pharmaceutical industry suffers in particular the consequences of polymorphism 1,2 . Active pharmaceutical ingredients are usually small, organic molecules that frequently exist in a plethora of crystalline forms. Different polymorphs can be patented separately and usually lead to different drug performances. Therefore a comprehensive screening of polymorphs is crucial to avoid a rival company from releasing to the market the same molecule in a different polymorph 3 , and to anticipate the transformation of one polymorph into another during the manufacturing process or the shelf life 4 .The screening of polymorphs was traditionally performed experimentally in spite of the large costs involved 2 . In the last 15 years the increase in computer power and the development of algorithms able to screen a large number of polymorphs has lead to a very significant successes in polymorph prediction 5-9 . Such methods are based on the search of local minima on the potential energy surface. The minima are ordered by energy and typically corrected for thermal effects using the harmonic approximation. However no method can claim to be able to scan exhaustively all the relevant low-lying minima. In addition, entropic effects beyond the harmonic approximation can be significant. Not only they a) Electronic mail: parrinello@phys.chem.ethz.ch can alter the delicate energetic balance between the different polymorphs but even stabilize structures that are not local minima of t...

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confidence: 95%

“…If one is interested in discovering new polymorphs this approach defeats the purpose. Recently, however, we have shown that in simple systems this can be circumvented by using as collective variable surrogates of enthalpy and entropy 11 . The idea is to mimic what happens in a real system in which there is a trade off between entropy and enthalpy.…”

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confidence: 99%

Predicting polymorphism in molecular crystals using orientational entropy

Piaggi

Parrinello

2018

Proc. Natl. Acad. Sci. U.S.A.

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107

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“…A large fraction of these methods depends on the definition of collective variables (CVs). Typical examples are umbrella sampling [2][3][4][5], metadynamics [6][7][8][9] and variationally enhanced sampling [10]. The efficiency of these simulations depends very much on the quality of the CV, and hence the finding and improving of CVs is the object of intense investigations [11].…”

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confidence: 99%

Improving collective variables: The case of crystallization

Zhang

Niu

Piccini

et al. 2019

The Journal of Chemical Physics

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Several enhanced sampling methods such as umbrella sampling or metadynamics rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis, the other on a variational approach to conformational dynamics, and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. However, the method based on linear discriminant analysis, in its harmonic version, is to be preferred because it is simpler and much less computationally demanding.

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“…The calculations of the fingerprint were done using a development version of PLUMED 2 41 . The RDF is calculated using a kernel density estimation of the radial distribution function 6,7 . Which for a Gaussian kernel is:…”

Section: Methodsmentioning

confidence: 99%

A local fingerprint for hydrophobicity and hydrophilicity: From methane to peptides

Pérez-Conesa

Piaggi

Parrinello

2019

The Journal of Chemical Physics

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An important characteristic that determines the behavior of a solute in water is whether it is hydrophobic or hydrophilic. The traditional classification is based on chemical experience and heuristics. However, this does not reveal how the local environment modulates this important property. We present a local fingerprint for hydrophobicity and hydrophilicity inspired by the two body contribution to the entropy. This fingerprint is an inexpensive, quantitative and physically meaningful way of studying hydrophilicity and hydrophobicity that only requires as input the water-solute radial distribution functions. We apply our fingerprint to octanol, benzene and the 20 proteinogenic amino acids. Our measure of hydrophilicity is coherent with chemical experience and, moreover, it also shows how the character of an atom can change as its environment is changed. Lastly, we use the fingerprint as a collective variable in a funnel metadynamics simulation of a hostguest system. The fingerprint serves as a desolvation collective variable that enhances transitions between the bound and unbound states.

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Enhancing Entropy and Enthalpy Fluctuations to Drive Crystallization in Atomistic Simulations

Cited by 97 publications

References 32 publications

Predicting polymorphism in molecular crystals using orientational entropy

Predicting polymorphism in molecular crystals using orientational entropy

Improving collective variables: The case of crystallization

A local fingerprint for hydrophobicity and hydrophilicity: From methane to peptides

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