Review Of Gender Differences In Learning Styles: Suggestions For STEM Education

Women have made great strides in baccalaureate degree obtainment, out numbering men by over 230,000 conferred baccalaureate degrees in 2008. However, the proportion of earned degrees for women in some of the Science, Technology, Engineering, and Mathematics (STEM) courses continues to lag behind male baccalaureate completions (National Science Foundation, 2010). In addition, according to the National Center for Women and Information Technology (NCWIT), only 21% of information and computer science degrees were awarded to women in 2006 (NCWIT, 2007). In the past decade, higher education has experienced a rapid decline in the number of women involved in the information sciences, particularly computer science (Bank, 2007). A number of social and educational factors have been considered barriers to women entering STEM fields and this area has been well studied in the literature. However, research examining the relationship between gender differences and learning styles in the context of these technical fields is limited. According to Kolb (1976), people decide on a major based on how well the norms of the major fit with their individual learning styles. This paper presents gender differences in learning styles and recommends teaching methodologies most preferred for female learners in STEM courses. Further, a survey was administered to ascertain the extent the results of this study support previous findings

Ice Sheet Sensing - Information Technology support and development

This report provides a summary of the PolarGrid geospatial support activities carried out by Jun Wang from July 2010 to June 2012 while working in the Digital Science Center of the Pervasive Technology Institute and later in the Science Gateway Group of Research Technology.This document reports work supported by: • The National Science Foundation under award number 0424589 (Principal Investigator: S. Prasad Gogineni), which supports the Center for Research in Ice Sheet Sensing CReSIS. The University of Kansas serves as the lead institution for CReSIS, which is comprised of six additional partner institutions: Elizabeth City State University, Indiana University, University of Washington, The Pennsylvania State University, Los Alamos National Laboratory, and the Association of Computer and Information Science Engineering Departments at Minority Institutions. • Indiana University with the support of a major award from the Lilly Endowment for “The Pervasive Technology Institute,” through the Digital Science Center led by Geoffrey C. Fox. Some of the work reported here was provided as matching effort for and in support of NSF award 0723054 - MRI: Acquisition of PolarGrid: Cyberinfrastructure for Polar Science (Principal Investigator: Geoffrey Fox; Co-Principal Investigators: Linda Hayden, Craig A. Stewart, Marlon Pierce, Malcolm LeCompte) • Indiana University through its funding for the Research Technologies Division of University Information Technologies Services, particularly the Science Gateway Group led by Marlon Pierce. Research Technologies is affiliated with the Pervasive Technology Institute as a Cyberinfrastructure and Service Center. Any opinions expressed in this document are those of the author and do not necessarily reflect the positions of any of the funding or supporting agencies and organizations

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

Combined analysis of Belle and Belle II data to determine the CKM angle phi(3) using B+ -> D(K(S)(0)h(+)h(-))h(+) decays

[EN] We present a measurement of the Cabibbo-Kobayashi-Maskawa unitarity triangle angle phi(3) (also known as gamma) using a model-independent Dalitz plot analysis of B+ -> D(K(S)(0)h(+)h(-))h(+), where D is either a D-0 or (D) over bar (0) meson and h is either a pi or K. This is the first measurement that simultaneously uses Belle and Belle II data, combining samples corresponding to integrated luminosities of 711 fb(-1) and 128 fb(-1), respectively. All data were accumulated from energy-asymmetric e(+)e(-) collisions at a centre-of-mass energy corresponding to the mass of the Upsilon(4S) resonance. We measure phi(3) = (78.4 +/- 11.4 +/- 0.5 +/- 1.0)degrees, where the first uncertainty is statistical, the second is the experimental systematic uncertainty and the third is from the uncertainties on external measurements of the D-decay strong-phase parameters.We thank Matt Kenzie for help with the GammaCombo package and Anita for calculating the effect of the Belle (II) acceptance on the values of ci and si. We thank the SuperKEKB group for the excellent operation of the accelerator; the KEK cryogenics group for the efficient operation of the solenoid; the KEK computer group for on-site computing support; and the raw-data centers at BNL, DESY, GridKa, IN2P3, and INFN for off-site computing support. This work was supported by the following funding sources: Science Committee of the Republic of Armenia Grant No. 20TTCG-1C010; Australian Research Council and research Grants No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, No. FT130100303, No. FT130100018, and No. FT120100745; Austrian Federal Ministry of Education, Science and Research, Austrian Science Fund No. P 31361-N36, and Horizon 2020 ERC Starting Grant No. 947006 "InterLeptons"; Natural Sciences and Engineering Research Council of Canada, Compute Canada and CANARIE; Chinese Academy of Sciences and research Grant No. QYZDJ-SSW-SLH011, National Natural Science Foundation of China and research Grants No. 11521505, No. 11575017, No. 11675166, No. 11761141009, No. 11705209, and No. 11975076, LiaoNing Revitalization Talents Program under Contract No. XLYC1807135, Shanghai Municipal Science and Technology Committee under Contract No. 19ZR1403000, Shanghai Pujiang Program under Grant No. 18PJ1401000, and the CAS Center for Excellence in Particle Physics (CCEPP); the Ministry of Education, Youth, and Sports of the Czech Republic under Contract No. LTT17020 and Charles University Grant No. SVV 260448; European Research Council, Seventh Framework PIEFGA-2013-622527, Horizon 2020 ERC-Advanced Grants No. 267104 and No. 884719, Horizon 2020 ERC-Consolidator Grant No. 819127, Horizon 2020 Marie Sklodowska-Curie Grant Agreement No. 700525 "NIOBE", and Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 Grant Agreement No. 822070 (European grants); L'Institut National de Physique Nucleaire et de Physique des Particules (IN2P3) du CNRS (France); BMBF, DFG, HGF, MPG, and AvH Foundation (Germany); Department of Atomic Energy under Project Identification No. RTI 4002 and Department of Science and Technology (India); Israel Science Foundation Grant No. 2476/17, U.S.-Israel Binational Science Foundation Grant No. 2016113, and Israel Ministry of Science Grant No. 3-16543; Istituto Nazionale di Fisica Nucleare and the research grants BELLE2; Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research Grants No. 16H03968, No. 16H03993, No. 16H06492, No. 16K05323, No. 17H01133, No. 17H05405, No. 18K03621, No. 18H03710, No. 18H05226, No. 19H00682, No. 26220706, and No. 26400255, the National Institute of Informatics, and Science Information NETwork 5 (SINET5), and the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan; National Research Foundation (NRF) of Korea Grants No. 2016R1D1A1B01010135, No. 2016R1D1A1B02012900, No. 2018R1A2B3003643, No. 2018R1A6A1A06024970, No. 2018R1D1A1B07047294, No. 2019K1A3A7A09033840, and No. 2019R1I1A3A01058933, Radiation Science Research Institute, Foreign Largesize Research Facility Application Supporting project, the Global Science Experimental Data Hub Center of the Korea Institute of Science and Technology Information and KREONET/GLORIAD; Universiti Malaya RU grant, Akademi Sains Malaysia, and Ministry of Education Malaysia; Frontiers of Science Program Contracts No. FOINS-296, No. CB221329, No. CB-236394, No. CB-254409, and No. CB-180023, and No. SEP-CINVESTAV research Grant No. 237 (Mexico); the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Science and Higher Education of the Russian Federation, Agreement No. 14.W03.31.0026, and the HSE University Basic Research Program, Moscow; University of Tabuk research Grants No. S-0256-1438 and No. S0280-1439 (Saudi Arabia); Slovenian Research Agency and research Grants No. J1-9124 and No. P1-0135; Agencia Estatal de Investigacion, Spain Grants No. FPA2014-55613P and No. FPA2017-84445-P, and No. CIDEGENT/2018/020 of Generalitat Valenciana; Ministry of Science and Technology and research Grants No. MOST106-2112-M-002-005MY3 and No. MOST107-2119-M-002-035-MY3, and the Ministry of Education (Taiwan); Thailand Center of Excellence in Physics; TUBITAK ULAKBIM (Turkey); National Research Foundation of Ukraine, project No. 2020.02/0257, and Ministry of Education and Science of Ukraine; the U.S. National Science Foundation and research Grants No. PHY1913789 and No. PHY-2111604, and the U.S. Department of Energy and research Awards No. DE-AC06-76RLO1830, No. DE-SC0007983, No. DE-SC0009824, No. DE-SC0009973, No. DE-SC0010007, No. DE-SC0010073, No. DE-SC0010118, No. DE-SC0010504, No. DESC0011784, No. DE-SC0012704, No. DE-SC0019230, No. DE-SC0021274; and the Vietnam Academy of Science and Technology (VAST) under Grant No. DL0000.05/21-23

HydroShare – A Case Study of the Application of Modern Software Engineering to a Large Distributed Federally-Funded Scientific Software Development Project

HydroShare is an online collaborative system under development to support the open sharing of hydrologic data, analytical tools, and computer models. With HydroShare, scientists can easily discover, access, and analyze hydrologic data and thereby enhance the production and reproducibility of hydrologic scientific results. HydroShare also takes advantage of emerging social media functionality to enable users to enhance information about and collaboration around hydrologic data and models. HydroShare is being developed by an interdisciplinary collaborative team of domain scientists, university software developers, and professional software engineers from ten institutions located across the United States. While the combination of non–co-located, diverse stakeholders presents communication and management challenges, the interdisciplinary nature of the team is integral to the project’s goal of improving scientific software development and capabilities in academia. This chapter describes the challenges faced and lessons learned with the development of HydroShare, as well as the approach to software development that the HydroShare team adopted on the basis of the lessons learned. The chapter closes with recommendations for the application of modern software engineering techniques to large, collaborative, scientific software development projects, similar to the National Science Foundation (NSF)–funded HydroShare, in order to promote the successful application of the approach described herein by other teams for other projects

The Data Conservancy: Building a Sustainable System for Interdisciplinary Scientific Data Curation and Preservation

Presentation at the PV 2009 conference in Madrid, SpainThe Data Conservancy (DC) is one of two awards through the US National Science Foundation’s DataNet program. The goal of the DataNet program is to create “a set of exemplar national and global data research infrastructure organizations (dubbed DataNet Partners) that provide unique opportunities to communities of researchers to advance sci- ence and/or engineering research and learning.” The DC embraces a shared vision: data curation is not an end, but rather a means to col- lect, organize, validate, and preserve data to address the grand research challenges that face society. The overarching goal of The Data Conservancy is to support new forms of inquiry and learning to meet these challenges through the creation, implementation, and sustained management of an integrated and comprehensive data curation strategy. DC will address this overarching goal with a comprehensive project comprising four inter- dependent threads: 1) infrastructure research and development, 2) computer science and information science research, 3) broader impacts, and 4) sustainability. The DC is led by the Sheridan Libraries at Johns Hopkins University. Working with the Sloan Digital Sky Survey data and the US National Virtual Observatory, the Sheridan Libraries have developed an initial architectural design, data models and metadata pro- files, and organizational models to support data curation. The DC will build upon these initial lessons learned from the partnership between the library and astronomy commu- nity and extend them into the life sciences, earth sciences, and social sciences. Use cases will provide the initial framework for technical requirements. A robust information sci- ence and computer science research agenda will highlight the scientific requirements and inform the development of a data framework for observations and a theoretical frame- work for data curation. These activities will guide the development of new curriculum at library and information science schools thereby building capacity for a new generation of data scientists. One of the central tenets of DC’s sustainability plan relates to the leadership role of the library. The Sheridan Libraries at Johns Hopkins University have established a leader- ship position in prototyping data curation systems and services, especially as they relate to astronomy. One of the key outcomes of DC will be a new model for libraries in the digital age. There are several fundamental implications for libraries in the realm of data curation as they relate to collections, services, and infrastructure. The North American Association of Research Libraries has already engaged the DC in its effort to consider these implications strategically as a means to transform the library’s role and contribu- tions toward building and sustaining data curation infrastructure.National Science Foundation, Office of Cyberinfrastructure DataNet Award #0830976; Institute of Museum and Library Services national leadership grant award LG0606018206; Microsoft Researc

The Ultrasound Window Into Vascular Ageing: A Technology Review by the VascAgeNet COST Action

Arteriosclerosis; Ultrasound; Vascular ageingArteriosclerosi; Ecografia; Envelliment vascularArteriosclerosis; Ecografía; Envejecimiento vascularNon-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker.This article is based upon work from COST Action CA18216 VascAgeNet, supported by COST (European Cooperation in Science and Technology, www.cost.eu). A.G. has received funding from “La Caixa” Foundation (LCF/BQ/PR22/11920008). R.E.C is supported by the National Health and Medical Research Council of Australia (reference: 2009005) and by a National Heart Foundation Future Leader Fellowship (reference: 105636). J.A. acknowledges support from the British Heart Foundation [PG/15/104/31913], the Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z], and the Cardiovascular MedTech Co-operative at Guy's and St Thomas' NHS Foundation Trust [MIC-2016-019]

Quantum Monte Carlo study of the energetics of the rutile, anatase, brookite, and columbite TiO2_{2} polymorphs

The relative energies of the low-pressure rutile, anatase, and brookite polymorphs and the high-pressure columbite polymorph of TiO$_{2}$ have been calculated as a function of temperature using the diffusion quantum Monte Carlo (DMC) method and density functional theory (DFT). The vibrational energies are found to be important on the scale of interest and significant quartic anharmonicity is found in the rutile phase. Static-lattice DFT calculations predict that anatase is lower in energy than rutile, in disagreement with experiment. The accurate description of electronic correlations afforded by DMC calculations and the inclusion of anharmonic vibrational effects contribute to stabilizing rutile with respect to anatase. Our calculations predict a phase transition from anatase to rutile TiO$_{2}$ at 630±210 K.J.R.T., P.L.R., and R.J.N. acknowledge financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant No. EP/J017639/1. B.M. acknowledges support from Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. R.M. is grateful for financial support from MEXT-KAKENHI Grants No. 26287063, No. 25600156, and No. 22104011, and a grant from the Asahi Glass Foundation. Computational resources were provided by the Archer facility of the U.K.'s national high-performance computing service (for which access was obtained via the UKCP consortium, EPSRC Grant No. EP/K014560/1), by the Center for Information Science of the JAIST, and by the K-computer (supported by the Computational Materials Science Initiative, CMSI/Japan, under Projects No. hp120086, No. hp140150, and No. hp150014)

Quantum Monte Carlo study of the energetics of the rutile, anatase, brookite, and columbite TiO2_{2} polymorphs

The relative energies of the low-pressure rutile, anatase, and brookite polymorphs and the high-pressure columbite polymorph of TiO$_{2}$ have been calculated as a function of temperature using the diffusion quantum Monte Carlo (DMC) method and density functional theory (DFT). The vibrational energies are found to be important on the scale of interest and significant quartic anharmonicity is found in the rutile phase. Static-lattice DFT calculations predict that anatase is lower in energy than rutile, in disagreement with experiment. The accurate description of electronic correlations afforded by DMC calculations and the inclusion of anharmonic vibrational effects contribute to stabilizing rutile with respect to anatase. Our calculations predict a phase transition from anatase to rutile TiO$_{2}$ at 630±210 K.J.R.T., P.L.R., and R.J.N. acknowledge financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant No. EP/J017639/1. B.M. acknowledges support from Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. R.M. is grateful for financial support from MEXT-KAKENHI Grants No. 26287063, No. 25600156, and No. 22104011, and a grant from the Asahi Glass Foundation. Computational resources were provided by the Archer facility of the U.K.'s national high-performance computing service (for which access was obtained via the UKCP consortium, EPSRC Grant No. EP/K014560/1), by the Center for Information Science of the JAIST, and by the K-computer (supported by the Computational Materials Science Initiative, CMSI/Japan, under Projects No. hp120086, No. hp140150, and No. hp150014)