Marcus A. Neumann

Bio: Marcus A. Neumann is an academic researcher from Symyx Technologies. The author has contributed to research in topics: Crystal structure prediction & Crystal structure. The author has an hindex of 23, co-authored 39 publications receiving 3089 citations. Previous affiliations of Marcus A. Neumann include Paris Descartes University.

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

Abstract: 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.

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

Abstract: 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crystal structures of the four systems, given only their molecular diagrams, while the recently determined but as-yet unpublished crystal structures were withheld by an independent referee.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.

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

Abstract: Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories – a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome.

X-Cell: a novel indexing algorithm for routine tasks and difficult cases

Abstract: X-Cell is a novel indexing algorithm that makes explicit use of systematic absences to search for possible indexing solutions from cells with low numbers of calculated reflections to cells with high numbers of reflections. Space groups with the same pattern of systematic absences are grouped together in powder extinction classes, and for a given peak number range an independent search is carried out in each powder extinction class. The method has the advantage that the correct cell is likely to be found before the rapid increase of possible solutions slows down the search significantly. A successive dichotomy approach is used to establish a complete list of all possible indexing solutions. The dichotomy procedure is combined with a search for the zero-point shift of the diffraction pattern, and impurity peaks can be dealt with by allowing for a user-defined portion of unindexed reflections. To rank indexing solutions with varying numbers of unindexed reflections, a new figure of merit is introduced that takes into account the highest level of agreement typically obtained for completely incorrect unit cells. The indexing of long and flat unit cells is facilitated by the possibility to search for rows or zones in reciprocal space first and then to use the lattice parameters of the dominant row or zone in the unit-cell search. The main advantages of X-Cell are robustness and completeness, as demonstrated by a validation study on a variety of compounds. The dominant phase of phase mixtures can be indexed in the presence of up to 50% of impurity peaks if high-quality synchrotron data are available.

A major advance in crystal structure prediction.

Abstract: The goal of predicting the crystal structure of an organic molecule from its molecular structure alone is of considerable industrial importance. The task is complicated, owing to the number of degrees of freedom to be explored, the complexities of intermolecular and intramolecular forces, and the difficulty in choosing a suitable computational criterion for identifying those crystal structures favored by nature. The difficulty of the task is clearly demonstrated by the regular “Crystal Structure Prediction Blind Test”, which is organized by the Cambridge Crystallographic Data Centre. A Blind Test has taken place in 1999, 2001, 2004, and recently in 2007. Participants are provided with three or four molecular structures and invited to predict, within six months, up to three crystal structures which they think each compound will adopt. The experimental crystal structures have been determined but are not available until after the participants have supplied their predictions. The limited number of successful predictions reported in the previous Blind Tests reveals just how difficult crystal structure prediction (CSP) can be. “Success” in this context means that the observed, experimental crystal structure is found among the three submitted predictions of a participant. All of the previous successful predictions were based on force-field methods, in which the intermolecular and intramolecular forces are represented by analytical functions. Herein, the successful application of a new CSP approach to all four compounds of the 2007 Blind Test is presented. The four compounds chosen for the 2007 Blind Test are shown in Scheme 1. In essence, two problems need to be addressed in CSP. First, there is the physical problem of accurately describing the relative stabilities of all possible crystal packing alternatives. Second, there is the mathematical problem of finding all low-lying minima on the lattice energy hypersurface, a function with many variables, including the unit cell dimensions, the space group, the number of molecules in the asymmetric unit, their conformation(s), and their packing in the crystal lattice. The high number of degrees of freedom results in a complex global optimization problem, for which several solution strategies have been put forward. The central part of the approach used herein is a hybrid method, developed by one of the authors (M.A.N.), for the calculation of lattice energies that combines density functional theory (DFT) simulations using the Vienna Ab initio Simulation Package (VASP) program with an empirical van der Waals (vdW) correction expressed in terms of a sum over isotropic atom–atom pair potentials. As solid-state DFT calculations are time-consuming and cannot be used directly for crystal structure generation, the hybrid method is used for the generation of reference data, from which a tailormade force field (TMFF) is derived for every molecule under consideration. The force field involves atomic point charges calculated from bond increments, isotropic vdW potentials, and covalent bond stretch, angle bend, torsion, and inversion terms. Non-equivalent atoms are attributed different forcefield atom types to allow for maximum customizability. The reference data include the electrostatic potential around the molecule, as well as energies and forces at, and around, local energy minima of densely packed crystal structures and isolated molecules in large simulation boxes. All force-field parameters, in particular bond increments and vdW constants, can be fitted to the reference data simultaneously. It is important that the TMFF provides a sufficiently faithful representation of the hybrid potential-energy surface, both in terms of structure and energetics. Consistency checks that enable this to be verified during the CSP will be described below. The TMFF provides lattice energies and forces to a crystal structure generation engine. The version used for the 2007 Blind Test combines a random structure generation mechanism with an efficient lattice-energy minimizer. Molecular flexibility is treated as an integral part of the crystal structure generation process, and all 230 space groups are considered. Scheme 1. Molecular structures of the 2007 Blind Test compounds. The Roman numerals refer to the numbering scheme used in the Blind Tests. Compound XV is a cocrystal.

Recent advances and applications of machine learning in solid-state materials science

Abstract: One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

Powder pattern indexing with the dichotomy method

Abstract: The efficiency of the successive dichotomy method for powder diffraction pattern indexing [Louer & Louer (1972). J. Appl. Cryst. 5, 271–275] has been proved over more than 30 years of usage. Features implemented in the new version of the computer program DICVOL04 include (i) a tolerance to the presence of impurity (or inaccurately measured) diffraction lines, (ii) a refinement of the `zero-point' position, (iii) a reviewing of all input lines from the solution found from, generally, the first 20 lines, (iv) a cell analysis, based on the concept of the reduced cell, to identify equivalent monoclinic and triclinic solutions, and (v) an optional analysis of input powder data to detect the presence of a significant `zero-point' offset. New search strategies have also been introduced, e.g. each crystal system is scanned separately, within the input volume limits, to limit the risk of missing a solution characterized by a metric lattice singularity. The default values in the input file have been extended to 25 A for the linear parameters and 2500 A3 for the cell volume. The search is carried out exhaustively within the input parameter limits and the absolute error on peak position measurements. Many tests with data from the literature and from powder data of pharmaceutical materials, collected with the capillary technique and laboratory monochromatic X-rays, have been performed with a high success rate, covering all crystal symmetries from cubic to triclinic. Some examples reported as `difficult' cases are also discussed. Additionally, a few recommendations for the correct practice of powder pattern indexing are reported.

Integrated materials design of organic semiconductors for field-effect transistors

Abstract: The past couple of years have witnessed a remarkable burst in the development of organic field-effect transistors (OFETs), with a number of organic semiconductors surpassing the benchmark mobility of 10 cm2/(V s). In this perspective, we highlight some of the major milestones along the way to provide a historical view of OFET development, introduce the integrated molecular design concepts and process engineering approaches that lead to the current success, and identify the challenges ahead to make OFETs applicable in real applications.

Crystal engineering: from molecule to crystal.

Abstract: How do molecules aggregate in solution, and how do these aggregates consolidate themselves in crystals? What is the relationship between the structure of a molecule and the structure of the crystal it forms? Why do some molecules adopt more than one crystal structure? Why do some crystal structures contain solvent? How does one design a crystal structure with a specified topology of molecules, or a specified coordination of molecules and/or ions, or with a specified property? What are the relationships between crystal structures and properties for molecular crystals? These are some of the questions that are being addressed today by the crystal engineering community, a group that draws from the larger communities of organic, inorganic, and physical chemists, crystallographers, and solid state scientists. This Perspective provides a brief historical introduction to crystal engineering itself and an assessment of the importance and utility of the supramolecular synthon, which is one of the most important concepts in the practical use and implementation of crystal design. It also provides a look to the future from the viewpoint of the author, and indicates some directions in which this field might be moving.