@article{7686359fac474bca99e4b5f089dd5dee,
title = "Reliable and practical computational description of molecular crystal polymorphs",
abstract = "Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs. However, the computational prediction of molecular crystal polymorphs is highly challenging due to the high dimensionality of conformational and crystallographic space accompanied by the need for relative free energies towithin 1 kJ/mol permolecule. In this study, we combine the most successful crystal structure sampling strategy with the most successful first-principles energy ranking strategy of the latest blind test of organic crystal structure prediction methods. Specifically,we present a hierarchical energy ranking approach intended for the refinement of relative stabilities in the final stage of a crystal structure prediction procedure. Such a combined approach provides excellent stability rankings for all studied systems and can be applied to molecular crystals of pharmaceutical importance.",
author = "Johannes Hoja and Ko, {Hsin Yu} and Neumann, {Marcus A.} and Roberto Car and DiStasio, {Robert A.} and Alexandre Tkatchenko",
note = "Funding Information: J.H. and A.T. acknowledge support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807 and the European Research Council Consolidator Grant BeStMo. H.-Y.K., R.A.D., and R.C. acknowledge support from the U.S. Department of Energy (DOE) under grant no. DE-SC0008626. R.A.D. also acknowledges partial support from start-up funding from Cornell University. This research used computational resources provided by the Argonne Leadership Computing Facility at Argonne National Laboratory (supported by the Office of Science of the DOE under contract no. DE-AC02-06CH11357), the National Energy Research Scientific Computing Center (supported by the Office of Science of the DOE under contract no. DE-AC02-05CH11231), the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University, the Fritz Haber Institute of the Max Planck Society (FHI-aims and Mercury), and the High Performance Computing facilities of the University of Luxembourg (see http://hpc.uni.lu). Publisher Copyright: {\textcopyright} 2019 American Association for the Advancement of Science. All rights reserved.",
year = "2019",
doi = "10.1126/sciadv.aau3338",
language = "English (US)",
volume = "45",
journal = "Acta Horticulturae Sinica",
issn = "0513-353X",
publisher = "Editorial Office of Horticultural Plant Journal",
number = "12",
}