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

Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; Cole, Jason C.; Cosburn, Katherine; Cuppen, H. M.; Curtis, Farren; Day, Graeme M.; DiStasio, Robert A.; Dzyabchenko, A. V.; Eijck, B.P. van; Elking, Dennis M.; Ende, Joost A. van den; Facelli, Julio C.; Ferraro, Marta B.; Füsti-Molnár, László; Gatsiou, Christina-Anna; Gee, Thomas S.; Gelder, René de; Ghiringhelli, Luca M.; Goto, Hitoshi; Grimme, Stefan; Guo, Rui; Hofmann, Detlef W. M.; Hoja, Johannes; Hylton, Rebecca K.; Iuzzolino, Luca; Jankiewicz, Wojciech; Jong, Daniël T. de; Kendrick, John; Klerk, Niek J. J. de; Ko, Hsin-Yu; Kuleshova, L. N.; Li, Xiayue; Lohani, Sanjaya; Leusen, Frank J. J.; Lund, Albert M.; Lv, Jian; Ma, Yanming; Marom, Noa; Masunov, Artëm E.; McCabe, Patrick; McMahon, David P.; Meekes, Hugo; Metz, Michael; Misquitta, Alston J.; Mohamed, Sharmarke; Monserrat, Bartomeu; Needs, R. J.; Neumann, Marcus A.; Nyman, Jonas; Obata, Shigeaki; Oberhofer, Harald; Oganov, Artem R.; Orendt, Anita M.; Pagola, Gabriel Ignacio; Pantelides, Constantinos C.; Pickard, Chris J.; Podeszwa, Rafał; Price, Louise S.; Price, Sarah L.; Pulido, Angeles; Read, Murray G.; Reuter, Karsten; Schneider, Elia; Schober, Christoph; Shields, Gregory P.; Singh, Pawanpreet; Sugden, Isaac J.; Szalewicz, Krzysztof; Taylor, Christopher R.; Tkatchenko, Alexandre; Tuckerman, Mark E.; Vacarro, Francesca; Vasileiadis, Manolis; Vázquez‐Mayagoitia, Álvaro; Vogt, Leslie; Wang, Yanchao; Watson, Rona E.; Wijs, G.A. de; Yang, Jack; Zhu, Qiang; Groom, Colin R.

doi:10.1107/s2052520616007447

Cited by 481 publications

(520 citation statements)

References 105 publications

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“…The ever increasing computer power allowed making many successful predictions of organic crystal structures [63]. The best starting point to keep track of the existing range of approaches is constituted by the accounts of the Blind Tests of CSP organized by the Cambridge Crystallographic Data Centre every few years [64]. These contests ask participants to predict the crystal structures of organic molecules starting from only the 2D molecular structure, which are then compared with the experimentally obtained, but not publicly released data to reveal the particularly effective techniques.…”

Section: Crystal Structure and Shape Predictionmentioning

confidence: 99%

“…These contests ask participants to predict the crystal structures of organic molecules starting from only the 2D molecular structure, which are then compared with the experimentally obtained, but not publicly released data to reveal the particularly effective techniques. Despite the excessive ramification of approaches developed to date, the whole CSP process can generally be subdivided into three distinct steps, each with their own methodologies and challenges: (1) conformational exploration of the molecule(s), (2) generation of candidate packing arrangements and (3) (re-)ranking of candidate structures using some form of fitness function [64]. Successful crystal engineering relies on the knowledge of molecular conformation, i.e., the overall shape of molecular building-blocks, as well as the relative arrangement of functional groups within a molecule that can participate in structure-directing interactions [65].…”

Section: Crystal Structure and Shape Predictionmentioning

confidence: 99%

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In Silico Prediction of Growth and Dissolution Rates for Organic Molecular Crystals: A Multiscale Approach

2017

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Solution crystallization and dissolution are of fundamental importance to science and industry alike and are key processes in the production of many pharmaceutical products, special chemicals, and so forth. The ability to predict crystal growth and dissolution rates from theory and simulation alone would be of a great benefit to science and industry but is greatly hindered by the molecular nature of the phenomenon. To study crystal growth or dissolution one needs a multiscale simulation approach, in which molecular-level behavior is used to parametrize methods capable of simulating up to the microscale and beyond, where the theoretical results would be industrially relevant and easily comparable to experimental results. Here, we review the recent progress made by our group in the elaboration of such multiscale approach for the prediction of growth and dissolution rates for organic crystals on the basis of molecular structure only and highlight the challenges and future directions of methodic development.

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Section: Crystal Structure and Shape Predictionmentioning

confidence: 99%

Section: Crystal Structure and Shape Predictionmentioning

confidence: 99%

In Silico Prediction of Growth and Dissolution Rates for Organic Molecular Crystals: A Multiscale Approach

2017

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“…32 Second, the community should organize blind-prediction competitions to assess data-driven fatigue models, analogous to the Sandia Fracture Challenge 33,34 or the long-running organic crystal structure prediction blind tests. 35 Finally, the community should adopt data platforms such as Citrination, 36 Materials Commons, 37 and Materials Data Facility, 38 as these systems greatly facilitate data and model sharing, reproducibility of results, and reuse of code.…”

Section: Recommendations To Foster Data-driven Fatigue Modelingmentioning

confidence: 99%

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Spear

Kalidindi

Meredig

et al. 2018

JOM

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“…15 We only mention in passing the prediction of organic molecular crystal structures, which has likewise seen fundamental breakthroughs. [16][17][18][19] Despite their predictive power, these structure searching methods are normally driven by quantum-mechanical density-functional theory (DFT) computations, and therefore they are limited to systems with relatively small unit cells. The allotropes of elemental phosphorus, [20][21][22] which are the topic of the present paper, directly illustrate the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

Data-driven learning and prediction of inorganic crystal structures

et al. 2018

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Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that a novel class of machine learning (ML) based interatomic potentials can provide a way out: by performing a highdimensional fit to the ab initio energy landscape, these potentials reach comparable accuracy but are orders of magnitude faster. In this paper, we develop our approach, dubbed Gaussian approximation potential-based random structure searching (GAP-RSS), towards a more general tool for exploring configuration spaces and predicting structures. We present a GAP-RSS interatomic potential model for elemental phosphorus, which identifies and correctly "learns" the orthorhombic black phosphorus (A17) structure without prior knowledge of any crystalline allotropes. Using the tubular structure of fibrous phosphorus as an example, we then discuss the limits of free searching, and discuss a possible way forward that combines a recently proposed fragment analysis with GAP-RSS. Examples of possible tubular (1D) and extended (3D) hypothetical allotropes of phosphorus as found by GAP-RSS are discussed. We believe that in the future, ML potentials could become versatile and routine computational tools for materials discovery and design.

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

Abstract: 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.

Cited by 481 publications

References 105 publications

In Silico Prediction of Growth and Dissolution Rates for Organic Molecular Crystals: A Multiscale Approach

In Silico Prediction of Growth and Dissolution Rates for Organic Molecular Crystals: A Multiscale Approach

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Data-driven learning and prediction of inorganic crystal structures

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