242 research outputs found
Competition between Icosahedral Motifs in AgCu, AgNi, and AgCo Nanoalloys: A Combined Atomistic-DFT Study
No full textThe structures of AgCu, AgNi, and AgCo nanoalloys with icosahedral geometry have been computationally studied by a combination of atomistic and density-functional theory (DFT) calculations, for sizes up to about 1400 atoms. These nanoalloys preferentially assume core-shell chemical ordering, with Ag in the shell. These core-shell nanoparticles can have either centered or off-center cores; they can have an atomic vacancy in their central site or present different arrangements of the Ag shell. Here we compare these different icosahedral motifs and determine the factors influencing their stability by means of a local strain analysis. The calculations find that off-center cores are favorable for sufficiently large core sizes and that the central vacancy is favorable in pure Ag clusters but not in binary clusters with cores of small size. A quite good agreement between atomistic and DFT calculations is found in most cases, with some discrepancy only for pentakis-dodecahedral structures. Our results support the accuracy of the atomistic model. Spin structure and charge transfer in the nanoparticles are also analyze
Rautananorakenteiden reaktiivisuus tiheysfunktionaaliteoriaan pohjautuen
No full textMost chemical processes rely on heterogeneous catalysis. The role of a catalyst is to provide an environment for more facile breaking and making of chemical bonds without being consumed itself. Catalytic performance is determined by the electronic structure, which can be modified by changing the atomic structure and composition. Conventional heterogeneous catalysis takes place on large crystal surfaces where the catalytic activity is determined mostly by the surface structure. However, catalyst particles smaller than 10 nm exhibit finite-size effects and the catalytic properties become very dependent on the size and geometry of the particle.
The size- and geometry-dependent properties allow the modification of nanostructures towards specific catalytic applications. To reach this goal the relationships between catalytic performance and atomic and electronic factors must be understood. In this thesis a quantum mechanical approach within density functional theory is employed to obtain an atomic level understanding of CO dissociation on iron nanostructures. This is an important initial step for the total efficiency and selectivity in, e.g., Fischer-Tropsch synthesis for hydrocarbons and synthesis of carbon nanotubes.
Results on 0.5-1.5 nm icosahedral, BCC, and amorphous iron nanoparticles show that the larger particles are more reactive towards CO dissociation. The presence of particle edges is essential for facile CO dissociation and on BCC particles very low dissociation barriers are observed even without atomic steps, which are the active sites on crystal surfaces. The reactivity of particle edges is explained by favorable orbital interactions between the metal surface and CO's LUMO orbital stabilizing the transition state. An optimal site for facile CO dissociation needs to fulfil several requirements: 1) CO is adsorbed at a BCC(100) like four-fold hollow site in the initial state,2) CO breaks over an atomic step, and 3) C is adsorbed in a fourfold site and O in a three-fold site in the final state.
All properties of magnetic materials depend on their magnetic state but the effect of different magnetic states on catalytic activity has been largely ignored. In this thesis it is shown that the electron donation and reactivity of FCC(111) iron surface can be modified by changing the magnetic state. This gives a justification for magnetically controlled catalysis.
Besides studies on reactivity of nanostructures, a general algorithm for improving transition state methods used in computational nanocatalysis was developed. The method removes external degrees of freedom using quaternion algebra and the new NEB-TR and DIMER-TR methods were shown to use fewer iterations to converge to a saddle point and describe minimum energy pathways more accurately in the case of NEB-TR.Suurin osa kemiallisista prosesseista perustuu heterogeeniseen katalyysiin, jossa katalyytin tehtävä on kiihdyttää kemiallisten sidosten muodostumista ja hajoamista kulumatta itse reaktion aikana. Katalyytin tehokkuus on suoraan sidoksissa sen elektronirakenteeseen, joka puolestaan riippuu sekä atomitason rakenteesta että koostumuksesta. Perinteiset heterogeeniset katalyytit ovat suuria metallipintoja, joiden aktiivisuus riippuu lähinnä katalyyttipinnan rakenteesta. Alle 10 nm katalyyttipartikkelien aktiivisuus on puolestaan hyvin riippuvainen partikkelin koosta ja geometriasta. Koko- ja geometriariippuvuus mahdollistaa katalyyttien muokkaamisen erilaisia sovelluksia varten. Tämä vaatii kuitenkin syvällisen ymmärryksen atomi- ja elektronirakenteen vaikutuksesta katalyyttiseen aktiivisuuteen.
Tässä väitöskirjassa on hyödynnetty kvanttimekaniikkaan pohjautuvaa tiheysfunktionaaliteoriaa antamaan atomitason ymmärrystä hiilimonoksidin hajoamisesta erilaisten rautananorakenteiden pinnalla. Tämän reaktioaskeleen on esitetty olevan keskeinen sekä selektiivisyyden että aktiivisuuden kannalta mm. pitkin hiilivetyjen Fischer Tropsch -synteesissä sekä hiilinanoputkien synteesissä.
Tutkimalla hiilimonoksidin hajoamista 0.5-1.5 nm rautananopartikkelien pinnalla havaittiin, että suuremmat partikkelit ovat reaktiivisempia ja että partikkelien reunat ovat keskeisiä reaktiivisuuden kannalta. Etenkin BCC-rakenteisten partikkelien reunat ovat hyvin reaktiivisia, mikä osoittaa, että katalyytti voi olla hyvin aktiivinen ilman atomikokoisia askelmia. Reunojen reaktiivisuus johtuu matalaenergisestä siirtymätilasta, mikä voitiin osoittaa orbitaaliteorian avulla. Hiilimonoksidin hajottamiseen vaadittavan aktiivisen kohdan tulee täyttää useita vaatimuksia:1) CO on sitoutunut BCC(100)-tyyppiseen kuoppapaikkaan, 2) CO hajoaa atomikokoisen askelman yli ja 3) hiili ja happi ovat vastaavasti neljän tai kolmen atomikuoppapaikoissa.
Magneettisten materiaalien ominaisuudet ovat hyvin riippuvaisia niiden magneettisesta rakenteesta. Magnetismin hyödyntämistä katalyysissä ei ole kuitenkaan juuri tutkittu. Tässä väitöskirjassa on osoitettu, että FCC(111) rautapinnan reaktiivisuus ja kyky luovuttaa elektroneja riippuu hyvin paljon sen magneettisesta rakenteesta. Tämä tulos osoittaa, että reaktiivisuutta voidaan muokata magnetismin avulla.
Reaktiivisuustutkimusten lisäksi tässä väitöskirjassa on kehitetty algoritmi, joka tehostaa siirtymätilojen etsintää laskennallisessa nanokatalyysissa. Menetelmä perustuu ulkoisten vapausasteiden eliminointiin kvaternioalgebran avulla. Uudet NEB-TR ja DIMER-TR menetelmät ovat tehokkaampia ja tarkempia kuin alkuperäiset menetelmät
Teoreettinen monimittakaavamallinnus Al2O3 ja ZnO ohutkalvojen atomikerroskasvatuksesta
No full textThe rapid development of nanotechnology, especially in the field of microelectronics, and ever shrinking dimensions of device components set high requirements for the manufacturing of the necessary nanostructures. Many microscopic components, e.g. transistors, are constructed layer-by-layer from thin film. An important tool 21st century technique for the fabrication of such thin films is the atomic layer deposition. Atomic layer deposition, originally developed in Finland, is based on sequential self-limiting gas-pulses, resulting in a uniform, pin-hole free thin film, with thickness control at the atomic level.
Computational modeling is an important part of modern chemistry. Research can be conducted theoretically - without empirical parameters - with the application of quantum mechanics. With quantum mechanical calculations it is possible to model the electronic structure of molecules and to study the bonding and interactions of molecules as well as different molecular mechanisms. In this work, the deposition of aluminium and zinc oxides were studied using computational chemistry. Both oxides have wide range of applications e.g. in transistors and solar cells.
Aluminium oxide is usually deposited using a trimethylaluminium-water-process. The surface chemistry was studied on a realistic hydroxylated surface model and trimethylaluminium was observed to react rapidly with surface hydroxyl groups to produce monomethylaluminium. Monomethylaluminium was estimated to be relatively inert and to convert to aluminium only at high temperatures. Subsequent water pulse mechanisms were also studied at low methyl-coverage. Direct dimethylaluminium--water reactions were accessible at process conditions, but the elimination of monomethylaluminium by water requires a complex cooperative mechanism.
Zinc oxide is usually deposited using a diethylzinc-water-process. Diethylzinc was found to convert rapidly into monoethylzinc but the elimination of monoethylzinc was found to be a slow process. Based on the calculations, two ethyl-saturated surface structures were constructed, corresponding to low and high temperature estimations. These saturated surfaces were used in a subsequent study on the water pulse reactions, resulting in a reaction network for a complete ALD cycle.
The growth of the zinc oxide thin film was then modeled in macroscopic scale using a kinetic Monte Carlo model. The kinetic modelling enables a direct comparison with experimental measurements. The kinetic model, built upon the theoretical calculations, accurately predicted the temperature-dependency of the film growth. Also, the predicted growth per cycle is in good agreement with experimental data.Nanotekniikan, erityisesti pienelektroniikan, nopea kehitys ja alati kutistuvat komponentit ovat asettaneet kovia vaatimuksia laitekomponenttien valmistusmenetelmille. Monet mikroskooppiset laitteet, esimerkiksi transistorit, valmistetaan kasvattamalla päällekäin erilaisia materiaali-kerrostumia, ohutkalvoja. Atomikerroskasvatus, ALD, on noussut tärkeäksi ohutkalvojen kasvatusprosessiksi 2000-luvulla. Atomikerroskasvatus on perujaan suomalainen prosessi, jossa kaasumaisia itseäänrajoittavia reagenssipulsseja vuorottemalla saadaan kasvatettua yhtenäinen ja sileä ohutkalvo, jonka paksuutta voidaan säädellä atomin tarkkuudella.
Laskennallinen mallintaminen tärkeä osa nykyajan kemian tutkimusta. Tutkimusta voidaan tehdä teoreettisesti - ilman empiirisiä parametreja - soveltamalla kvanttimekaniikkaa. Kvantti-mekaniikalla päästään käsiksi molekyylien elektronirakenteeseen ja voidaan tutkia molekyylien sitoutumista, vuorovaikutusta sekä erilaisia reaktiomekanismeja. Tässä työssä on tutkittu laskennallisesti alumiini- ja sinkkioksidiohutkalvojen kasvatusta ALD:llä. Molemmilla oksideilla on lukuisia teollisia sovelluskohteita, mm. transistoreissa ja aurinkokennoissa.
Alumiinioksidia kasvatetaan yleisesti trimetyylialumiini-vesi-prosessilla. Prosessin pintakemiaa tutkittiin realistisella veden peittämällä pintamallilla ja trimetyylialumiinin havaittiin reagoivan nopeasti pinnalla monometyylialumiiniksi. Monometyylialumiinin arvioitiin reagoivan alumiiniksi vasta korkeissa lämpötiloissa. Veden todettiin voivan reagoida dimetyylialumiinin kanssa reaktori-olosuhteissa. Monometyylialumiinin eliminointi on sen sijaan mutkikkaampi ja vaatii yhtäaikaa useamman vesimolekyylin.
Sinkkioksidia kasvatetaan vastaavasti dietyylisinkki-vesi-prosessilla. Dietyylisinkin reagoi pinnalla nopeasti monoetyylisinkiksi, mutta monoetyylisinkin eliminoiti on kohtalaisen hidas prosessi. Reaktiomekanismien pohjalta rakennettiin kaksi etyylin peittämää pintarakennetta, jotka vastasivat pinnan saturoitumista matalassa ja korkeassa lämpötilassa. Näitä etyylin peittämiä pintoja hyödynnettiin vesipulssin reaktiomekanismeja tutkimiseen. Sinkkioksidin kasvulle saatiin näin rakennettua koko ALD kierroksen kattava reaktiomekanismiverkosto.
Sinkkioksidiohutkalvon kasvua mallinnettiin makroskooppisessa mittakaavassa kineettisen Monte Carlo -mallin avulla. Kineettinen malli mahdollistaa helpon vertailun kokeellisten mittausten kanssa. Teoreettisista reaktiolaskuista rakennettu kineettinen malli kykeni ennustamaan ohutkalvon kasvun lämpötila-riippuvuuden tarkasti. Myös mallista laskettu kierroskasvu vastaa hyvin kokeellisia tuloksia
Sustainable International Business:A Retrospection and Future Research Direction
No full textThe first section of the book addresses the interaction between international business activities and the economic aspect of sustainability. The first finding of this part of the book is that international business affects and is affected by economic and other pillars of sustainability. For example, in the chapter “Realisation of SDGs in Africa: An Impactful Political CSR Approach,” the author developed a political CSR model and contended that rejuvenating the SDGs in Africa through an impactful PCSR model can unleash the huge potential of international business in the realization of SDGs. In the chapter “Value Creation Impact: Role of Stakeholders in the Development of Sustainable Foreign Trade,” the author revealed that pursuing sustainability in an organization increases sustainable competitive advantage by improving global value chains and the perception of consumers and other market stakeholders. Second, embracing the economic aspect of sustainability requires reshaping the global supply chain functions and value chain activities. For example, in the chapter “Reshaping the World’s Supply Chain? A Case Study of Vietnam’s PAN Group Adopting the Circular Economy Concept,” the authors pointed out that sustainability, circular economy, and supply chain are interconnected concepts that are pivotal in promoting responsible and efficient resource management. In the chapter “Integration of Internal Audit and Sustainability Functions: A Business Model Suggestion,” in order to resolve significant disruptions and inefficiencies in the purchasing processes, the authors developed a novel business model that brings together different areas of expertise, prevents overlapping and duplication of purchasing tasks, and improves interdepartmental communication. Third, this part of the book revealed that firm-level digital capability helps internationalizing firms achieve sustainable economic development by facilitating access to foreign markets. For example, in the chapters “Mitigating the Negative Implications of Fake Social Media News on Internationalizing Firms: The Role of Social Media Capability” and “Network Ties and Opportunity Recognition in SME Internationalization in the Social Media Context,” the author found that social media capability facilitates internationalizing SMEs identify international opportunities, access foreign market information, and enhance the institutional legitimacy in foreign markets
Density functional theory study of trends in water dissociation on oxygen-preadsorbed and pure transition metal surfaces
No full textFunding Information: This work was supported by Business Finland through project Molecular Modelling in Industrial Research and Development (MM-IRD). The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources. Publisher Copyright: © 2023 The Author(s)Oxygen and water are the most reactive gases of the ambient air. The adsorption of both molecules on transition metal surfaces have been studied extensively, but mostly separately. However, water and oxygen usually co-exist, and therefore realistic systems need to take into consideration both simultaneously. As these adsorption reactions are so common, state-of-the-art results are beneficial as they capture large trends as accurately as possible. A comprehensive study of oxygen and water co-adsorption and dissociation on Ag(111)-, Au(111)-, Pd(111)-, Pt(111)-, Rh(111)- and Ni(111)-surfaces have been performed using density functional theory. We present a very strong general trend, where dissociated oxygen systematically lowers the activation energy of water dissociation on transition metal surfaces. This makes the oxygen dissociation the rate-determining step of the water dissociation reaction. The effect is caused by the additional pathway that the dissociated oxygen enables for the dissociation of water molecule.Peer reviewe
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
No full textFor reproducing the results presented in "Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.
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This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.
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The content of a directory is shown in a tree-like format:
├── 1DMDQ
│ ├── 1_md
│ │ ├── dft-common-params.inc
│ │ ├── dmdq-md-pos-1.xyz
│ │ ├── md.inp
│ │ ├── pos.xyz
│ │ ├── submit.sh
│ │ └── subsys.inc
│ ├── 2_cdftaimd
│ │ ├── state_a
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ ├── state_b
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ └── subsys.inc
│ │ └── state_c
│ │ ├── becke_twoconstraints.inc
│ │ ├── cdft_md.bash
│ │ ├── cdft_md.inp
│ │ ├── dft-common-params.inc
│ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ └── subsys.inc
│ └── 3_cdft_wH2O_sccs
│ ├── state_a
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ ├── state_b
│ │ ├── b_to_a.tar.gz
│ │ └── b_to_c.tar.gz
│ └── state_c
│ ├── framePrint.py
│ ├── input_files
│ │ ├── 1_energy_cdft_STATE1.bash
│ │ ├── 2_energy_cdft_STATE2.bash
│ │ ├── 3_energy_cdft_mixed.bash
│ │ ├── becke_twoconstraints.inc
│ │ ├── dft-common-params.inc
│ │ ├── energy_cdft.inp
│ │ ├── energy_mixed_cdft.inp
│ │ └── subsys.inc
│ └── README
├── 2MeVi
│ ├── 1_md
│ │ ├── dft-common-params.inc
│ │ ├── md.inp
│ │ ├── mevi-md-pos-1.xyz
│ │ ├── pos.xyz
│ │ ├── submit.sh
│ │ └── subsys.inc
│ ├── 2_cdftaimd
│ │ ├── state_a
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ └── subsys.inc
│ │ ├── state_b
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ └── state_c
│ │ ├── becke_twoconstraints.inc
│ │ ├── cdft_md.bash
│ │ ├── cdft_md.inp
│ │ ├── dft-common-params.inc
│ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ ├── frame.xyz
│ │ └── subsys.inc
│ └── 3_cdft_wH2O_sccs
│ ├── state_a
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ ├── state_b
│ │ ├── b_to_a
│ │ │ ├── framePrint.py
│ │ │ ├── input_files
│ │ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ │ ├── becke_twoconstraints.inc
│ │ │ │ ├── dft-common-params.inc
│ │ │ │ ├── energy_cdft.inp
│ │ │ │ ├── energy_mixed_cdft.inp
│ │ │ │ └── subsys.inc
│ │ │ └── README
│ │ └── b_to_c
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ └── state_c
│ ├── framePrint.py
│ ├── input_files
│ │ ├── 1_energy_cdft_STATE1.bash
│ │ ├── 2_energy_cdft_STATE2.bash
│ │ ├── 3_energy_cdft_mixed.bash
│ │ ├── becke_twoconstraints.inc
│ │ ├── dft-common-params.inc
│ │ ├── energy_cdft.inp
│ │ ├── energy_mixed_cdft.inp
│ │ └── subsys.inc
│ └── README
├── 3OHVi
│ ├── 1_md
│ │ ├── dft-common-params.inc
│ │ ├── md.inp
│ │ ├── ohvi-md-pos-1.xyz
│ │ ├── pos.xyz
│ │ ├── submit.sh
│ │ └── subsys.inc
│ ├── 2_cdftaimd
│ │ ├── state_a
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ ├── state_b
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ └── state_c
│ │ ├── becke_twoconstraints.inc
│ │ ├── cdft_md.bash
│ │ ├── cdft_md.inp
│ │ ├── dft-common-params.inc
│ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ ├── frame.xyz
│ │ └── subsys.inc
│ └── 3_cdft_wH2O_sccs
│ ├── state_a
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ ├── state_b
│ │ ├── b_to_a
│ │ │ ├── framePrint.py
│ │ │ ├── input_files
│ │ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ │ ├── becke_twoconstraints.inc
│ │ │ │ ├── dft-common-params.inc
│ │ │ │ ├── energy_cdft.inp
│ │ │ │ ├── energy_mixed_cdft.inp
│ │ │ │ └── subsys.inc
│ │ │ └── README
│ │ ├── b_to_a.tar.gz
│ │ ├── b_to_c
│ │ │ ├── framePrint.py
│ │ │ ├── input_files
│ │ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ │ ├── becke_twoconstraints.inc
│ │ │ │ ├── dft-common-params.inc
│ │ │ │ ├── energy_cdft.inp
│ │ │ │ ├── energy_mixed_cdft.inp
│ │ │ │ └── subsys.inc
│ │ │ └── README
│ │ └── b_to_c.tar.gz
│ └── state_c
│ ├── framePrint.py
│ ├── input_files
│ │ ├── 1_energy_cdft_STATE1.bash
│ │ ├── 2_energy_cdft_STATE2.bash
│ │ ├── 3_energy_cdft_mixed.bash
│ │ ├── becke_twoconstraints.inc
│ │ ├── dft-common-params.inc
│ │ ├── energy_cdft.inp
│ │ ├── energy_mixed_cdft.inp
│ │ └── subsys.inc
│ └── README
├── 4dBR5
│ ├── 1_md
│ │ ├── dft-common-params.inc
│ │ ├── dmdq-md-pos-1.xyz
│ │ ├── md.inp
│ │ ├── pos.xyz
│ │ ├── submit.sh
│ │ └── subsys.inc
│ ├── 2_cdftaimd
│ │ ├── state_a
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ ├── state_b
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ └── state_c
│ │ ├── becke_twoconstraints.inc
│ │ ├── cdft_md.bash
│ │ ├── cdft_md.inp
│ │ ├── dft-common-params.inc
│ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ ├── frame.xyz
│ │ └── subsys.inc
│ └── 3_cdft_wH2O_sccs
│ ├── state_a
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ ├── state_b
│ │ ├── b_to_a.tar.gz
│ │ └── b_to_c.tar.gz
│ └── state_c
│ ├── framePrint.py
│ ├── input_files
│ │ ├── 1_energy_cdft_STATE1.bash
│ │ ├── 2_energy_cdft_STATE2.bash
│ │ ├── 3_energy_cdft_mixed.bash
│ │ ├── becke_twoconstraints.inc
│ │ ├── dft-common-params.inc
│ │ ├── energy_cdft.inp
│ │ ├── energy_mixed_cdft.inp
│ │ └── subsys.inc
│ └── README
├── 52HNQ
│ ├── 1_md
│ │ ├── dft-common-params.inc
│ │ ├── hnq-md-pos-1.xyz
│ │ ├── md.inp
│ │ ├── pos.xyz
│ │ ├── submit.sh
│ │ └── subsys.inc
│ ├── 2_cdftaimd
│ │ ├── state_a
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ ├── state_b
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── cdft_md.bash
│ │ │ ├── cdft_md.inp
│ │ │ ├── dft-common-params.inc
│ │ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ │ ├── frame.xyz
│ │ │ └── subsys.inc
│ │ └── state_c
│ │ ├── becke_twoconstraints.inc
│ │ ├── cdft_md.bash
│ │ ├── cdft_md.inp
│ │ ├── dft-common-params.inc
│ │ ├── frame-cdft-pos-total.xyz.tar.gz
│ │ ├── frame.xyz
│ │ └── subsys.inc
│ └── 3_cdft_wH2O_sccs
│ ├── state_a
│ │ ├── framePrint.py
│ │ ├── input_files
│ │ │ ├── 1_energy_cdft_STATE1.bash
│ │ │ ├── 2_energy_cdft_STATE2.bash
│ │ │ ├── 3_energy_cdft_mixed.bash
│ │ │ ├── becke_twoconstraints.inc
│ │ │ ├── dft-common-params.inc
│ │ │ ├── energy_cdft.inp
│ │ │ ├── energy_mixed_cdft.inp
│ │ │ └── subsys.inc
│ │ └── README
│ ├── state_b
│ │ ├── b_to_a.tar.gz
│ │ └── b_to_c.tar.gz
│ └── state_c
│ ├── framePrint.py
│ ├── input_files
│ │ ├── 1_energy_cdft_STATE1.bash
│ │ ├── 2_energy_cdft_STATE2.bash
│ │ ├── 3_energy_cdft_mixed.bash
│ │ ├── becke_twoconstraints.inc
│ │ ├── dft-common-params.inc
│ │ ├── energy_cdft.inp
│ │ ├── energy_mixed_cdft.inp
│ │ └── subsys.inc
│ └── README
└── 6_n_H2O_effect_mevi
├── 08h2o
│ ├── framePrint.py
│ ├── README
│ ├── state_a.tar.gz
│ └── state_b.tar.gz
├── 10h2o
│ ├── framePrint.py
│ ├── README
│ ├── state_a.tar.gz
│ └── state_b.tar.gz
├── 20h2o
│ ├── framePrint.py
│ ├── README
│ ├── state_a.tar.gz
│ └── state_b.tar.gz
├── 40h2o
│ ├── framePrint.py
│ ├── README
│ ├── state_a.tar.gz
│ └── state_b.tar.gz
├── 97h2o
│ ├── framePrint.py
│ ├── README
│ ├── state_a.tar.gz
│ └── state_b.tar.gz
└── fig3.png
74 directories, 301 files
-------------------------------------------------------
There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.
The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again
Mining Cell Transition Data
No full textCell transition data is obtained from a cellular phone that switches its current serving cell tower. The data consists of a sequence of transition events, which are pairs of cell identifiers and transition times. The focus of this thesis is applying data mining methods to such data, developing new algorithms, and extracting knowledge that will be a solid foundation on which to build location-aware applications.
In addition to a thorough exploration of the features of the data, the tools and methods developed in this thesis provide solutions to three distinct research problems. First, we develop clustering algorithms that produce a reliable mapping between cell transitions and physical locations observed by users of mobile devices. The main clustering algorithm operates in online fashion, and we consider also a number of offline clustering methods for comparison.
Second, we define the concept of significant locations, known as bases, and give an online algorithm for determining them. Finally, we consider the task of predicting the movement of the user, based on historical data. We develop a prediction algorithm that considers paths of movement in their entirety, instead of just the most recent movement history. All of the presented methods are evaluated with a significant body of real cell transition data, collected from about one hundred different individuals.
The algorithms developed in this thesis are designed to be implemented on a mobile device, and require no extra hardware sensors or network infrastructure. By not relying on external services and keeping the user information as much as possible on the user s own personal device, we avoid privacy issues and let the users control the disclosure of their location information.Matkapuhelinverkoissa tukiasemat palvelevat tietyllä alueella olevia puhelimia. Tukiaseman radiosignaalin kattamaa aluetta kutsutaan soluksi. Vaihtaessaan käyttämäänsä tukiasemaa puhelin tuottaa solusiirtymädataa, joka koostuu sarjasta solutunnisteita ja siirtymisaikoja. Vaikka data ei sisälläkään paikkojen koordinaatteja, siitä voidaan silti oppia tunnistamaan puhelimen käyttäjälle tärkeitä paikkoja sekä ennustamaan hänen liikkumistaan paikkojen välillä.
Väitöskirjatyössä sovelletaan tiedonlouhinnan menetelmiä solusiirtymädataan ja tavoitteena on tuottaa työkaluja paikkatietoisten sovellusten kehittämiseen. Saatuja tuloksia testataan aineistolla, joka on kerätty noin sadalta matkapuhelimen käyttäjältä.
Datassa on erityispiirteitä, jotka tekevät sen käsittelystä haastavaa. Yksi keskeinen ongelma on se, että solun vaihtuminen ei aina johdu käyttäjän liikkumisesta, vaan puhelin voi vaihtaa käyttämäänsä tukiasemaa myös muista syistä. Ja kääntäen: yksittäisen solun sisällä on mahdollista liikkua jonkin matkaa ennen solun vaihtumista. Solujen ja fyysisten paikkojen vastaavuuksien löytämiseksi työssä esitellään klusterointimenetelmä, joka yhdistää samaan paikkaan todennäköisesti liittyviä soluja suuremmiksi kokonaisuuksiksi. Kyseessä on ns. online-algoritmi, joka analysoi solusiirtymävirtaa ja päivittää klusterien joukkoa reaaliaikaisesti.
Työn tuloksena syntyneet algoritmit voidaan toteuttaa käytännössä kaikilla mobiililaitealustoilla, eikä niiden käyttö vaadi toimenpiteitä puhelinoperaattorilta tai -valmistajalta. Koska menetelmät vaativat vain vähän puhelimen laskenta- ja tallennuskapasiteettia, käyttäjän paikkatietoja ei tarvitse lähettää puhelimesta eteenpäin jatkokäsittelyä varten. Tämä turvaa puhelimen käyttäjän yksityisyyttä, sillä käyttäjä voi päättää itse, kenelle ja kuinka paljon tietoja luovutetaan.ei saavutettav
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