Works by Lordi, Vincenzo

Results: 9

Behavior of Na and RbF‐Treated CdS/Cu(In,Ga)Se2 Solar Cells with Stress Testing under Heat, Light, and Junction Bias.

Published in:

Physica Status Solidi - Rapid Research Letters, 2021, v. 15, n. 2, p. 1, doi. 10.1002/pssr.202000530

By:

Walkons, Curtis;
Jahandardoost, Mohsen;
Friedlmeier, Theresa M.;
Hempel, Wolfram;
Paetel, Stefan;
Nardone, Marco;
Ursprung, Benedikt;
Barnard, Edward S.;
Kweon, Kyoung E.;
Lordi, Vincenzo;
Bansal, Shubhra

Publication type:

Article

Incident wavelength and polarization dependence of spectral shifts in β-Ga2O3 UV photoluminescence.

Published in:

Scientific Reports, 2018, v. 8, n. 1, p. 1, doi. 10.1038/s41598-018-36676-7

By:

Wang, Yunshan;
Dickens, Peter T.;
Varley, Joel B.;
Ni, Xiaojuan;
Lotubai, Emmanuel;
Sprawls, Samuel;
Liu, Feng;
Lordi, Vincenzo;
Krishnamoorthy, Sriram;
Blair, Steve;
Lynn, Kelvin G.;
Scarpulla, Michael;
Sensale-Rodriguez, Berardi

Publication type:

Article

The role of oxygen doping on elemental intermixing at the PVD‐CdS/Cu (InGa)Se2 heterojunction.

Published in:

Progress in Photovoltaics, 2019, v. 27, n. 3, p. 255, doi. 10.1002/pip.3087

By:

He, Xiaoqing;
Ercius, Peter;
Varley, Joel;
Bailey, Jeff;
Zapalac, Geordie;
Nagle, Tim;
Poplavskyy, Dmitry;
Mackie, Neil;
Bayman, Atiye;
Lordi, Vincenzo;
Rockett, Angus

Publication type:

Article

<italic>TopoMS</italic>: Comprehensive topological exploration for molecular and condensed‐matter systems.

Published in:

Journal of Computational Chemistry, 2018, v. 39, n. 16, p. 936, doi. 10.1002/jcc.25181

By:

Bhatia, Harsh;
Gyulassy, Attila G.;
Lordi, Vincenzo;
Pask, John E.;
Pascucci, Valerio;
Bremer, Peer‐Timo

Publication type:

Article

ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data.

Published in:

NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-024-01497-y

By:

Lindsey, Rebecca K.;
Bastea, Sorin;
Hamel, Sebastien;
Lyu, Yanjun;
Goldman, Nir;
Lordi, Vincenzo

Publication type:

Article

Cross-scale covariance for material property prediction.

Published in:

NPJ Computational Materials, 2025, v. 11, n. 1, p. 1, doi. 10.1038/s41524-024-01453-w

By:

Jasperson, Benjamin A.;
Nikiforov, Ilia;
Samanta, Amit;
Zhou, Fei;
Tadmor, Ellad B.;
Lordi, Vincenzo;
Bulatov, Vasily V.

Publication type:

Article

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.

Published in:

Nature Communications, 2025, v. 16, n. 1, p. 1, doi. 10.1038/s41467-025-59232-0

By:

Schwalbe-Koda, Daniel;
Hamel, Sebastien;
Sadigh, Babak;
Zhou, Fei;
Lordi, Vincenzo

Publication type:

Article

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.

Published in:

Nature Communications, 2025, v. 16, n. 1, p. 1, doi. 10.1038/s41467-025-59232-0

By:

Schwalbe-Koda, Daniel;
Hamel, Sebastien;
Sadigh, Babak;
Zhou, Fei;
Lordi, Vincenzo

Publication type:

Article

Anomalous diffusion along metal/ceramic interfaces.

Published in:

Nature Communications, 2018, v. 9, n. 1, p. 1, doi. 10.1038/s41467-018-07724-7

By:

Kumar, Aakash;
Barda, Hagit;
Klinger, Leonid;
Finnis, Michael W.;
Lordi, Vincenzo;
Rabkin, Eugen;
Srolovitz, David J.

Publication type:

Article

Works by Lordi, Vincenzo

Behavior of Na and RbF‐Treated CdS/Cu(In,Ga)Se<sub>2</sub> Solar Cells with Stress Testing under Heat, Light, and Junction Bias.

Incident wavelength and polarization dependence of spectral shifts in β-Ga<sub>2</sub>O<sub>3</sub> UV photoluminescence.

The role of oxygen doping on elemental intermixing at the PVD‐CdS/Cu (InGa)Se<sub>2</sub> heterojunction.

<italic>TopoMS</italic>: Comprehensive topological exploration for molecular and condensed‐matter systems.

ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data.

Cross-scale covariance for material property prediction.

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.

Anomalous diffusion along metal/ceramic interfaces.