Long-timescale simulations of H2_2O admolecule diffusion on Ice Ih(0001) surfaces

Long-timescale simulations of the diffusion of a H$_2$O admolecule on the (0001) basal plane of ice Ih were carried out over a temperature range of 100 to 200 K using the adaptive kinetic Monte Carlo method and TIP4P/2005f interaction potential function. The arrangement of dangling H atoms was varied from the proton-disordered surface to the perfectly ordered Fletcher surface. A large variety of sites was found leading to a broad distribution in adsorption energy at both types of surfaces. Up to 4 % of the sites on the proton-disordered surface have an adsorption energy exceeding the cohesive energy of ice Ih. The mean squared displacement of a simulated trajectory at 175 K for the proton-disordered surface gave a diffusion constant of 6$\cdot$10$^{-10}$ cm$^2$/s, consistent with an upper bound previously reported from experimental measurements. During the simulation, dangling H atoms were found to rearrange so as to reduce clustering, thereby approaching a linear Fletcher type arrangement. Diffusion on the perfectly ordered Fletcher surface was estimated to be significantly faster, especially in the direction along the rows of dangling hydrogen atoms. From simulations over the range in temperature, an effective activation energy of diffusion was estimated to be 0.16 eV and 0.22 eV for diffusion parallel and perpendicular to the rows, respectively. Even a slight disruption of the rows of the Fletcher surface made the diffusion isotropic.Comment: 24 pages, 8 figures, 1 tabl

CO diffusion into amorphous H2O ices

The mobility of atoms, molecules, and radicals in icy grain mantles regulates ice restructuring, desorption, and chemistry in astrophysical environments. Interstellar ices are dominated by H2O, and diffusion on external and internal (pore) surfaces of H2O-rich ices is therefore a key process to constrain. This study aims to quantify the diffusion kinetics and barrier of the abundant ice constituent CO into H2O-dominated ices at low temperatures (15–23 K), by measuring the mixing rate of initially layered H2O(:CO2)/CO ices. The mixed fraction of CO as a function of time is determined by monitoring the shape of the infrared CO stretching band. Mixing is observed at all investigated temperatures on minute timescales and can be ascribed to CO diffusion in H2O ice pores. The diffusion coefficient and final mixed fraction depend on ice temperature, porosity, thickness, and composition. The experiments are analyzed by applying Fick's diffusion equation under the assumption that mixing is due to CO diffusion into an immobile H2O ice. The extracted energy barrier for CO diffusion into amorphous H2O ice is ~160 K. This is effectively a surface diffusion barrier. The derived barrier is low compared to current surface diffusion barriers in use in astrochemical models. Its adoption may significantly change the expected timescales for different ice processes in interstellar environments.Astronom

An Ice Age JWST inventory of dense molecular cloud ices

Icy grain mantles are the main reservoir of the volatile elements that link chemical processes in dark, interstellar clouds with the formation of planets and composition of their atmospheres. The initial ice composition is set in the cold, dense parts of molecular clouds, prior to the onset of star formation. With the exquisite sensitivity of JWST, this critical stage of ice evolution is now accessible for detailed study. Here we show the first results of the Early Release Science program "Ice Age" that reveal the rich composition of these dense cloud ices. Weak ices, including, $^{13}$CO$_2$, OCN$^-$, $^{13}$CO, OCS, and COMs functional groups are now detected along two pre-stellar lines of sight. The $^{12}$CO$_2$ ice profile indicates modest growth of the icy grains. Column densities of the major and minor ice species indicate that ices contribute between 2 and 19% of the bulk budgets of the key C, O, N, and S elements. Our results suggest that the formation of simple and complex molecules could begin early in a water-ice rich environment.Comment: To appear in Nature Astronomy on January 23rd, 2023. 33 pages, 16 figures, 3 tables; includes extended and supplemental data sections. Part of the JWST Ice Age Early Release Science program's science enabling products. Enhanced spectra downloadable on Zenodo at the following DOI: 10.5281/zenodo.750123

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

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.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447

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

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 submissions, 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

Mechanism of Phase Transition in dl-Methionine: Determining Cooperative and Molecule-by-Molecule Transformations

The solid-state phase transition in dl-methionine has been extensively studied because of its atypical behavior. The transition occurs through changes in the molecular conformation and 3D packing of the molecules. Phase transitions in racemic aliphatic amino acid crystals are known to show different behaviors depending on whether conformational changes or packing changes are involved, where the former is thought to proceed through a nucleation-and-growth mechanism in a standard molecule-by-molecule picture, and the latter through a cooperative mechanism. The phase transition of dl-methionine resembles the thermodynamic, kinetic, and structural features of both categories: a conformational change and relative shifts between layers in two directions. The present paper presents molecular dynamics simulations of the phase transition to examine the underlying mechanism from two perspectives: (i) analysis of the scaling behavior of the free energy barriers involved in the phase transition and (ii) a structural inspection of the phase transition. Both methods can help to distinguish between a concerted phase change and a molecule-by-molecule or zip-like mechanism. The free energy predominantly scales with the system size, which suggests a cooperative mechanism. The structural changes draw, however, a slightly more complex picture. The conformational changes appear to occur in a molecule-by-molecule fashion, where the rotational movement is triggered by movement in the same layer. Conformational changes occur on a time scale nearly twice as long as the shifts between layers. Shifts in one direction appear to be less concerted than shifts in the perpendicular direction. We relate this to the edge-free energy involved in these shifts. We believe that the behavior observed in dl-methionine is likely applicable to phase transitions in other layered systems that interact through aliphatic chains as well

Astrochemistry: Synthesis and Modelling

We discuss models that astrochemists have developed to study the chemical composition of the interstellar medium. These models aim at computing the evolution of the chemical composition of a mixture of gas and dust under astrophysical conditions. These conditions, as well as the geometry and the physical dynamics, have to be adapted to the objects being studied because different classes of objects have very different characteristics (temperatures, densities, UV radiation fields, geometry, history etc); e.g., proto-planetary disks do not have the same characteristics as proto-stellar envelopes. Chemical models are being improved continually thanks to comparisons with observations but also thanks to laboratory and theoretical work in which the individual processes are studied