Aaltodoc Repository :: Browsing by Author "Caro, Miguel A."

Browsing by Author "Caro, Miguel A."

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Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW
(AMERICAN CHEMICAL SOCIETY, 2022-07-13) Golze, Dorothea; Hirvensalo, Markus; Hernández-León, Patricia; Aarva, Anja; Etula, Jarkko; Susi, Toma; Rinke, Patrick; Laurila, Tomi; Caro, Miguel A.; Department of Applied Physics; Department of Electrical Engineering and Automation; Department of Chemistry and Materials Science; Computational Electronic Structure Theory; Microsystems Technology; Physical Characteristics of Surfaces and Interfaces; Centre of Excellence in Quantum Technology, QTF; Department of Applied Physics; University of Vienna
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with GW and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.
Accurate schemes for calculation of thermodynamic properties of liquid mixtures from molecular dynamics simulations
(2016-12-28) Caro, Miguel A.; Laurila, Tomi; Lopez-Acevedo, Olga; Department of Applied Physics; Department of Electrical Engineering and Automation; Computational Electronic Structure Theory; Computational Soft and Molecular Matter; Microsystems Technology
We explore different schemes for improved accuracy of entropy calculations in aqueous liquid mixtures from molecular dynamics (MD) simulations. We build upon the two-phase thermodynamic (2PT) model of Lin et al. [J. Chem. Phys. 119, 11792 (2003)] and explore new ways to obtain the partition between the gas-like and solid-like parts of the density of states, as well as the effect of the chosen ideal “combinatorial” entropy of mixing, both of which have a large impact on the results. We also propose a first-order correction to the issue of kinetic energy transfer between degrees of freedom (DoF). This problem arises when the effective temperatures of translational, rotational, and vibrational DoF are not equal, either due to poor equilibration or reduced system size/time sampling, which are typical problems for ab initio MD. The new scheme enables improved convergence of the results with respect to configurational sampling, by up to one order of magnitude, for short MD runs. To ensure a meaningful assessment, we perform MD simulations of liquid mixtures of water with several other molecules of varying sizes: methanol, acetonitrile, N, N-dimethylformamide, and n-butanol. Our analysis shows that results in excellent agreement with experiment can be obtained with little computational effort for some systems. However, the ability of the 2PT method to succeed in these calculations is strongly influenced by the choice of force field, the fluidicity (hard-sphere) formalism employed to obtain the solid/gas partition, and the assumed combinatorial entropy of mixing. We tested two popular force fields, GAFF and OPLS with SPC/E water. For the mixtures studied, the GAFF force field seems to perform as a slightly better “all-around” force field when compared to OPLS+SPC/E.
Addressing Dynamics at Catalytic Heterogeneous Interfaces with DFT-MD: Anomalous Temperature Distributions from Commonly Used Thermostats
(AMERICAN CHEMICAL SOCIETY, 2022-03-24) Korpelin, Ville; Kiljunen, Toni; Melander, Marko M.; Caro, Miguel A.; Kristoffersen, Henrik H.; Mammen, Nisha; Apaja, Vesa; Honkala, Karoliina; Department of Chemistry; Department of Electrical Engineering and Automation; Microsystems Technology; Centre of Excellence in Quantum Technology, QTF; University of Jyväskylä; University of Copenhagen
Density functional theory-based molecular dynamics (DFT-MD) has been widely used for studying the chemistry of heterogeneous interfacial systems under operational conditions. We report frequently overlooked errors in thermostated or constant-temperature DFT-MD simulations applied to study (electro)catalytic chemistry. Our results demonstrate that commonly used thermostats such as Nosé-Hoover, Berendsen, and simple velocity-rescaling methods fail to provide a reliable temperature description for systems considered. Instead, nonconstant temperatures and large temperature gradients within the different parts of the system are observed. The errors are not a "feature"of any particular code but are present in several ab initio molecular dynamics implementations. This uneven temperature distribution, due to inadequate thermostatting, is well-known in the classical MD community, where it is ascribed to the failure in kinetic energy equipartition among different degrees of freedom in heterogeneous systems (Harvey et al. J. Comput. Chem. 1998, 726-740) and termed the flying ice cube effect. We provide tantamount evidence that interfacial systems are susceptible to substantial flying ice cube effects and demonstrate that the traditional Nosé-Hoover and Berendsen thermostats should be applied with care when simulating, for example, catalytic properties or structures of solvated interfaces and supported clusters. We conclude that the flying ice cube effect in these systems can be conveniently avoided using Langevin dynamics.
Atomistic analysis of the impact of alloy and well-width fluctuations on the electronic and optical properties of InGaN/GaN quantum wells
(2015) Schulz, Stefan; Caro, Miguel A.; Coughlan, Conor; "O'Reilly", Eoin P.; Department of Applied Physics; Department of Electrical Engineering and Automation; Computational Soft and Molecular Matter
We present an atomistic description of the electronic and optical properties of In0.25Ga0.75N/GaN quantum wells. Our analysis accounts for fluctuations of well width, local alloy composition, strain and built-in field fluctuations as well as Coulomb effects. We find a strong hole and much weaker electron wave function localization in InGaN random alloy quantum wells. The presented calculations show that while the electron states are mainly localized by well-width fluctuations, the holes states are already localized by random alloy fluctuations. These localization effects affect significantly the quantum well optical properties, leading to strong inhomogeneous broadening of the lowest interband transition energy. Our results are compared with experimental literature data.
Atomistic description of wave function localization effects in In(x)Ga(1-x)N alloys and quantum wells
(2015) Schulz, Stefan; Marquardt, Oliver; Coughlan, Conor; Caro, Miguel A.; Brandt, Oliver; "O'Reilly", Eoin P.; Department of Applied Physics; Department of Electrical Engineering and Automation
Cluster-based multidimensional scaling embedding tool for data visualization
(Institute of Physics Publishing, 2024-05-09) Hernandez Leon, Patricia; Caro, Miguel A.; Department of Chemistry and Materials Science; DAS Group
We present a new technique for visualizing high-dimensional data called cluster MDS (cl-MDS), which addresses a common difficulty of dimensionality reduction methods: preserving both local and global structures of the original sample in a single 2-dimensional visualization. Its algorithm combines the well-known multidimensional scaling (MDS) tool with the k-medoids data clustering technique, and enables hierarchical embedding, sparsification and estimation of 2-dimensional coordinates for additional points. While cl-MDS is a generally applicable tool, we also include specific recipes for atomic structure applications. We apply this method to non-linear data of increasing complexity where different layers of locality are relevant, showing a clear improvement in their retrieval and visualization quality.
Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
(2018) Deringer, Volker L.; Caro, Miguel A.; Jana, Richard; Aarva, Anja; Elliott, Stephen R.; Laurila, Tomi; Csányi, Gábor; Pastewka, Lars; Department of Electrical Engineering and Automation; Department of Applied Physics; Centre of Excellence in Quantum Technology, QTF; Microsystems Technology; University of Cambridge; University of Freiburg
Tetrahedral amorphous carbon (ta-C) is widely used for coatings because of its superior mechanical properties and has been suggested as an electrode material for detecting biomolecules. Despite extensive research, however, the complex atomic-scale structures and chemical reactivity of ta-C surfaces are incompletely understood. Here, we combine machine learning, density functional tight binding, and density functional theory simulations to shed new light on this long-standing problem. We make atomistic models of ta-C surfaces, characterize them by local structural fingerprints, and provide a library of structures at different system sizes. We then move beyond the pure element and exemplify how chemical reactivity (hydrogenation and oxidation) can be modeled at the surfaces. Our work opens up new perspectives for modeling the surfaces and interfaces of amorphous solids, which will advance studies of ta-C and other functional materials.
Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties
(IOP Publishing Ltd., 2021-10-27) Leppänen, Elli; Aarva, Anja; Sainio, Sami; Caro, Miguel A.; Laurila, Tomi; Department of Electrical Engineering and Automation; Department of Chemistry and Materials Science; Microsystems Technology; Centre of Excellence in Quantum Technology, QTF
Connecting a material's surface chemistry with its electrocatalytic performance is one of the major questions in analytical electrochemistry. This is especially important in many sensor applications where analytes from complex media need to be measured. Unfortunately, today this connection is still largely missing except perhaps for the most simple ideal model systems. Here we present an approach that can be used to obtain insights about this missing connection and apply it to the case of carbon nanomaterials. In this paper we show that by combining advanced computational techniques augmented by machine learning methods with x-ray absorption spectroscopy (XAS) and electrochemical measurements, it is possible to obtain a deeper understanding of the correlation between local surface chemistry and electrochemical performance. As a test case we show how by computationally assessing the growth of amorphous carbon (a-C) thin films at the atomic level, we can create computational structural motifs that may in turn be used to deconvolute the XAS data from the real samples resulting in local chemical information. Then, by carrying out electrochemical measurements on the same samples from which x-ray spectra were measured and that were further characterized computationally, it is possible to gain insight into the interplay between the local surface chemistry and electrochemical performance. To demonstrate this methodology, we proceed as follows: after assessing the basic electrochemical properties of a-C films, we investigate the effect of short HNO3 treatment on the sensitivity of these electrodes towards an inner sphere redox probe dopamine to gain knowledge about the influence of altered surface chemistry to observed electrochemical performance. These results pave the way towards a more general assessment of electrocatalysis in different systems and provide the first steps towards data driven tailoring of electrode surfaces to gain optimal performance in a given application.
Doping as a means to probe the potential dependence of dopamine adsorption on carbon-based surfaces: A first-principles study
(2017-06-21) Aarva, Anja; Laurila, Tomi; Caro, Miguel A.; Department of Electrical Engineering and Automation; Department of Applied Physics; Microsystems Technology
In this work, we study the adsorption characteristics of dopamine (DA), ascorbic acid (AA), and dopaminequinone (DAox) on carbonaceous electrodes. Our goal is to obtain a better understanding of the adsorption behavior of these analytes in order to promote the development of new carbon-based electrode materials for sensitive and selective detection of dopamine in vivo. Here we employ density functional theory-based simulations to reach a level of detail that cannot be achieved experimentally. To get a broader understanding of carbonaceous surfaces with different morphological characteristics, we compare three materials: graphene, diamond, and amorphous carbon (a-C). Effects of solvation on adsorption characteristics are taken into account via a continuum solvent model. Potential changes that take place during electrochemical measurements, such as cyclic voltammetry, can also alter the adsorption behavior. In this study, we have utilized doping as an indirect method to simulate these changes by shifting the work function of the electrode material. We demonstrate that sp2- and sp3-rich materials, as well as a-C, respond markedly different to doping. Also the adsorption behavior of the molecules studied here differs depending on the surface material and the change in the surface potential. In all cases, adsorption is spontaneous, but covalent bonding is not detected in vacuum. The aqueous medium has a large effect on the adsorption behavior of DAox, which reaches its highest adsorption energy on diamond when the potential is shifted to more negative values. In all cases, inclusion of the solvent enhances the charge transfer between the slab and DAox. Largest differences in adsorption energy between DA and AA are obtained on graphene. Gaining better understanding of the behavior of the different forms of carbon when used as electrode materials provides a means to rationalize the observed complex phenomena taking place at the electrodes during electrochemical oxidation/reduction of these biomolecules.
Energy band alignment and electronic states of amorphous carbon surfaces in vacuo and in aqueous environment
(2015) Caro, Miguel A.; Määttä, Jukka; Lopez-Acevedo, Olga; Laurila, Tomi; Department of Chemistry; Department of Applied Physics; Department of Electrical Engineering and Automation; Soft Materials Modelling; Computational Soft and Molecular Matter; Microsystems Technology
Experiment-Driven Atomistic Materials Modeling : A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon
(American Chemical Society, 2024-05-29) Zarrouk, Tigany; Ibragimova, Rina; Bartók, Albert P.; Caro, Miguel A.; Department of Chemistry and Materials Science; DAS Group; University of Warwick
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
(Nature Publishing Group, 2023-07-27) Marchant, George A.; Caro, Miguel A.; Karasulu, Bora; Pártay, Livia B.; University of Warwick; Department of Chemistry and Materials Science; Department of Chemistry and Materials Science
We demonstrate how the many-body potential energy landscape of carbon can be explored with the nested sampling algorithm, allowing for the calculation of its pressure-temperature phase diagram. We compare four interatomic potential models: Tersoff, EDIP, GAP-20 and its recently updated version, GAP-20U. Our evaluation is focused on their macroscopic properties, melting transitions, and identifying thermodynamically stable solid structures up to at least 100 GPa. The phase diagrams of the GAP models show good agreement with experimental results. However, we find that the models’ description of graphite includes thermodynamically stable phases with incorrect layer spacing. By adding a suitable selection of structures to the database and re-training the potential, we have derived an improved model — GAP-20U+gr — that suppresses erroneous local minima in the graphitic energy landscape. At extreme high pressure nested sampling identifies two novel stable structures in the GAP-20 model, however, the stability of these is not confirmed by electronic structure calculations, highlighting routes to further extend the applicability of the GAP models.
Fully analytic valence force field model for the elastic and inner elastic properties of diamond and zincblende crystals
(American Physical Society, 2019-09-30) Tanner, Daniel S.P.; Caro, Miguel A.; Schulz, Stefan; O'Reilly, Eoin P.; Department of Electrical Engineering and Automation; Department of Applied Physics; Microsystems Technology; Centre of Excellence in Quantum Technology, QTF; Tyndall National Institute; University College Cork
Using a valence force field model based on that introduced by Martin, we present three related methods through which we analytically determine valence force field parameters. The methods introduced allow easy derivation of valence force field parameters in terms of the Kleinman parameter ζ and bulk properties of zincblende and diamond crystals. We start with a model suited for covalent and weakly ionic materials, where the valence force field parameters are derived in terms of ζ and the bulk elastic constants C11, C12, and C44. We show that this model breaks down as the material becomes more ionic and specifically when the elastic anisotropy factor A=2C44/(C11-C12)>2. The analytic model can be stabilized for ionic materials by including Martin's electrostatic terms with effective cation and anion charges in the valence force field model. Inclusion of effective charges determined via the optical phonon mode splitting provides a stable model for all but two of the materials considered (zincblende GaN and AlN). A stable model is obtained for all materials considered by also utilizing the inner elastic constant E11 to determine the magnitude of the effective charges used in the Coulomb interaction. Test calculations show that the models describe well structural relaxation in superlattices and alloys and reproduce key phonon band structure features.
Gaussian approximation potentials: Theory, software implementation and application examples
(American Institute of Physics, 2023-11-07) Klawohn, Sascha; Darby, James P.; Kermode, James R.; Csányi, Gábor; Caro, Miguel A.; Bartók, Albert P.; Department of Chemistry and Materials Science; DAS Group; University of Warwick; University of Cambridge
Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements.
A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles
(American Institute of Physics, 2023-04-07) Kloppenburg, Jan; Pártay, Livia B.; Jónsson, Hannes; Caro, Miguel A.; Department of Chemistry and Materials Science; Department of Electrical Engineering and Automation; Department of Applied Physics; DAS Group; Centre of Excellence in Quantum Technology, QTF; University of Warwick
A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
(Nature Publishing Group, 2020-10-29) Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor; Department of Applied Physics; Department of Electrical Engineering and Automation; Microsystems Technology; University of Oxford; University of Cambridge
Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an outstanding challenge. Here, we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum-mechanical results. Our model is fitted to density-functional theory plus many-body dispersion (DFT + MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of “phosphorene” and “hittorfene”); its transferability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale exemplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chemistry, physics, and materials science.
Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon
(2018) Caro, Miguel A.; Deringer, Volker L.; Koskinen, Jari; Laurila, Tomi; Csányi, Gábor; Department of Electrical Engineering and Automation; Department of Applied Physics; Department of Chemistry and Materials Science; Centre of Excellence in Quantum Technology, QTF; Physical Characteristics of Surfaces and Interfaces; Microsystems Technology; University of Cambridge
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp3 fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp3 content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp3 fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power.
Hybrid carbon based nanomaterials for electrochemical detection of biomolecules
(2017) Laurila, Tomi; Sainio, Sami; Caro, Miguel A.; Department of Electrical Engineering and Automation; Department of Applied Physics; Microsystems Technology
By combining different allotropic forms of carbon at the nanoscale it is possible to fabricate tailor made surfaces with unique properties. These novel materials have shown high potential especially in the electrochemical detection of different biomolecules, such as dopamine, glutamate and ascorbic acid, which are important neurotransmitters in the mammalian central nervous system. Thus, more information about their material properties must be obtained in order to realize their high potential to the maximum. The results presented in this review clearly point out that although there is an extensive amount of data available on the structural, chemical and electrochemical properties on different carbon nanoforms, the data are scattered, often inconsistent and even contradictory. Hybrid carbon nanomaterials are much less investigated than the individual allotropes, but based on the existing data they possess extremely interesting electrochemical properties. Thus, it is of utmost importance to carry out extensive step-by-step characterization of these materials by utilizing combination of detailed computational and experimental work. In this way it will become possible to avoid approaches to material design that are based solely on trial-and-error approach, which has, unfortunately, been more a rule than an exception.
Hybrid functional study of nonlinear elasticity and internal strain in zinc-blende III-V materials
(American Physical Society, 2019-01-10) Tanner, Daniel S.P.; Caro, Miguel A.; Schulz, Stefan; O'Reilly, Eoin P.; Tyndall National Institute; Centre of Excellence in Quantum Technology, QTF; University College Cork; Department of Applied Physics; Department of Electrical Engineering and Automation
We investigate the elastic properties of selected zinc-blende III-V semiconductors. Using hybrid functional density functional theory, we calculate the second- and third-order elastic constants and first- and second-order internal strain tensor components for Ga, In, and Al containing III-V compounds. For many of these parameters, there are no available experimental measurements, and this work is the first to predict their values. The stricter convergence criteria for the calculation of higher-order elastic constants are demonstrated, and arguments are made based on this for extracting these constants via the calculated stresses, rather than the energies, in the context of plane-wave-based calculations. The calculated elastic properties are used to determine the strain regime at which higher-order elasticity becomes important by comparing the stresses predicted by a lower- and a higher-order elasticity theory. Finally, the results are compared with available experimental literature data and previous theory.
Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C
(IOP Publishing Ltd., 2023-04) Caro, Miguel A.; DAS Group; Department of Chemistry and Materials Science; Department of Electrical Engineering and Automation
Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si.

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