Anja Aarva

Bio: Anja Aarva is an academic researcher from Aalto University. The author has contributed to research in topics: Amorphous carbon & Density functional theory. The author has an hindex of 8, co-authored 14 publications receiving 285 citations. Previous affiliations of Anja Aarva include Helsinki University of Technology & VTT Technical Research Centre of Finland.

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Abstract: Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp2 motifs with markedly different reactivities. We therefore draw a ...

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

Abstract: 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.

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

Abstract: Carbonaceous materials, especially tetrahedral amorphous carbon (ta-C), can form complex functionalized surface structures and are thus promising candidates for applications in biomedical devices and electrochemistry. Functional groups at ta-C surfaces have been widely studied by spectroscopic techniques; however, interpretation of the experimental data is extremely difficult, especially in the case of X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The assignments of experimental XPS and XAS signals are normally based on references obtained from molecular or crystalline samples, which are simplified approximations for the far more complex amorphous structures. Here, we use extensive density functional theory (DFT) simulations to predict XAS and XPS signatures for carbon-based materials in more realistic environments, building on large data sets of structural models generated by a machine-learning (ML) interatomic potential. The results indicate clear signatures: individual...

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

Abstract: Carbon-based nanomaterials are a promising platform for diverse technologies, but their rational design requires a more detailed chemical control over their structure and properties than is current...

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

Abstract: Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research.

Efficient electrically powered CO2-to-ethanol via suppression of deoxygenation

Abstract: The carbon dioxide electroreduction reaction (CO2RR) provides ways to produce ethanol but its Faradaic efficiency could be further improved, especially in CO2RR studies reported at a total current density exceeding 10 mA cm−2. Here we report a class of catalysts that achieve an ethanol Faradaic efficiency of (52 ± 1)% and an ethanol cathodic energy efficiency of 31%. We exploit the fact that suppression of the deoxygenation of the intermediate HOCCH* to ethylene promotes ethanol production, and hence that confinement using capping layers having strong electron-donating ability on active catalysts promotes C–C coupling and increases the reaction energy of HOCCH* deoxygenation. Thus, we have developed an electrocatalyst with confined reaction volume by coating Cu catalysts with nitrogen-doped carbon. Spectroscopy suggests that the strong electron-donating ability and confinement of the nitrogen-doped carbon layers leads to the observed pronounced selectivity towards ethanol. The electroreduction of CO2 to ethanol could enable the clean production of fuels using renewable power. This study shows how confinement effects from nitrogen-doped carbon layers on copper catalysts enable selective ethanol production from CO2 with a Faradaic efficiency of up to 52%.

Quantum Chemistry in the Age of Machine Learning.

Abstract: As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.

Gaussian Process Regression for Materials and Molecules.

Abstract: We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.