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Data‐Driven Compound Identification in Atmospheric Mass Spectrometry.
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- Advanced Science, 2024, v. 11, n. 8, p. 1, doi. 10.1002/advs.202306235
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- Article
Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules.
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- Scientific Data, 2023, v. 10, n. 1, p. 1, doi. 10.1038/s41597-023-02366-x
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- Article
Lithium‐Ion Battery Technology for Voltage Control of Perpendicular Magnetization.
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- Advanced Functional Materials, 2022, v. 32, n. 29, p. 1, doi. 10.1002/adfm.202113118
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- Article
Machine learning sparse tight-binding parameters for defects.
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- NPJ Computational Materials, 2022, v. 8, n. 1, p. 1, doi. 10.1038/s41524-022-00791-x
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Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning.
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- Atmospheric Chemistry & Physics, 2021, v. 21, n. 17, p. 13227, doi. 10.5194/acp-21-13227-2021
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- Article
Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations.
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- Advanced Functional Materials, 2021, v. 31, n. 32, p. 1, doi. 10.1002/adfm.202010853
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Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations (Adv. Funct. Mater. 32/2021).
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- Advanced Functional Materials, 2021, v. 31, n. 32, p. 1, doi. 10.1002/adfm.202170235
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- Article
Predicting Gas-Particle Partitioning Coefficients of Atmospheric Molecules with Machine Learning.
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- Atmospheric Chemistry & Physics Discussions, 2021, p. 1, doi. 10.5194/acp-2020-1258
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- Article
Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search.
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- Advanced Science, 2020, v. 7, n. 15, p. 1, doi. 10.1002/advs.202000992
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- Article
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules.
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- Scientific Data, 2020, v. 7, n. 1, p. 1, doi. 10.1038/s41597-020-0385-y
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- Article
Data‐Driven Materials Science: Status, Challenges, and Perspectives.
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- Advanced Science, 2020, v. 7, n. 2, p. N.PAG, doi. 10.1002/advs.201903667
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- Article
Data‐Driven Materials Science: Status, Challenges, and Perspectives.
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- Advanced Science, 2019, v. 6, n. 21, p. N.PAG, doi. 10.1002/advs.201900808
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- Article
5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019).
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- Advanced Science, 2019, v. 6, n. 21, p. N.PAG, doi. 10.1002/advs.201970125
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- Article
Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019).
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- Advanced Science, 2019, v. 6, n. 9, p. N.PAG, doi. 10.1002/advs.201970053
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- Article
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.
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- Advanced Science, 2019, v. 6, n. 9, p. N.PAG, doi. 10.1002/advs.201801367
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- Article
Bayesian inference of atomistic structure in functional materials.
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- NPJ Computational Materials, 2019, v. 5, n. 1, p. N.PAG, doi. 10.1038/s41524-019-0175-2
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Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials.
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- NPJ Computational Materials, 2018, v. 4, n. 1, p. N.PAG, doi. 10.1038/s41524-018-0107-6
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Optical Properties of Silver-Mediated DNA from Molecular Dynamics and Time Dependent Density Functional Theory.
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- International Journal of Molecular Sciences, 2018, v. 19, n. 8, p. 2346, doi. 10.3390/ijms19082346
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- Article
Band Bending Engineering at Organic/Inorganic Interfaces Using Organic Self-Assembled Monolayers.
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- Advanced Electronic Materials, 2017, v. 3, n. 6, p. 1, doi. 10.1002/aelm.201600373
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On the Monte Carlo Description of Hot Carrier Effects and Device Characteristics of III-N LEDs.
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- Advanced Electronic Materials, 2017, v. 3, n. 6, p. 1, doi. 10.1002/aelm.201600494
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Special Issue: Materials Platform at Aalto University.
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- Advanced Electronic Materials, 2017, v. 3, n. 6, p. 1, doi. 10.1002/aelm.201700202
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Corrigendum: Evidence for photogenerated intermediate hole polarons in ZnO.
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- Nature Communications, 2015, v. 6, n. 6, p. 7694, doi. 10.1038/ncomms8694
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Evidence for photogenerated intermediate hole polarons in ZnO.
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- Nature Communications, 2015, v. 6, n. 4, p. 6901, doi. 10.1038/ncomms7901
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Random-phase approximation and its applications in computational chemistry and materials science.
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- Journal of Materials Science, 2012, v. 47, n. 21, p. 7447, doi. 10.1007/s10853-012-6570-4
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Exciting prospects for solids: Exact-exchange based functionals meet quasiparticle energy calculations.
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- Physica Status Solidi (B), 2008, v. 245, n. 5, p. 929, doi. 10.1002/pssb.200743380
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- Article