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@misc{Ouyang,
author={Ouyang, Runhai},
title={{GitHub - rouyang2017/SISSO: A data-driven method combining symbolic regression and compressed sensing toward accurate {\&} interpretable models}},
url={https://github.com/rouyang2017/SISSO},
urldate={2021-09-02}
}
@article{Ouyang2019a,
abstract={The identification of descriptors of materials properties and functions that capture the underlying physical mechanisms is a critical goal in data-driven materials science. Only such descriptors will enable a trustful and efficient scanning of materials spaces and possibly the discovery of new materials. Recently, the sure-independence screening and sparsifying operator (SISSO) has been introduced and was successfully applied to a number of materials-science problems. SISSO is a compressed-sensing based methodology yielding predictive models that are expressed in form of analytical formulas, built from simple physical properties. These formulas are systematically selected from an immense number (billions or more) of candidates. In this work, we describe a powerful extension of the methodology to a 'multi-task learning' approach, which identifies a single descriptor capturing multiple target materials properties at the same time. This approach is specifically suited for a heterogeneous materials database with scarce or partial data, e.g., in which not all properties are reported for all materials in the training set. As showcase examples, we address the construction of materials-properties maps for the relative stability of octet-binary compounds, considering several crystal phases simultaneously, and the metal/insulator classification of binary materials distributed over many crystal-prototypes.},
archivePrefix={arXiv},
arxivId={1901.00948},
author={Ouyang, Runhai and Ahmetcik, Emre and Carbogno, Christian and Scheffler, Matthias and Ghiringhelli, Luca M.},
doi={10.1088/2515-7639/ab077b},
eprint={1901.00948},
file={:home/purcell/Documents/Mendeley Desktop/Ouyang et al. - Journal of Physics Materials - 2019.pdf:pdf},
abstract={Modern first-principles calculations predict that the thermal conductivity of boron arsenide is second only to that of diamond, the best thermal conductor, which may be of benefit for waste heat management in electronic devices. With the optimization of single-crystal growth methods, large-size and high-quality boron arsenide single crystals have been grown and thermal conductivity measurements have verified the related predictions. Benefiting from the increased size and improved qualities, additional properties have been characterized. Important factors related to boron arsenide, remaining challenges, and the future outlook are addressed in this minireview.},
author={Tian, Fei and Ren, Zhifeng},
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abstract={Raman microscopy in the lattice phonon region coupled with X-ray diffraction have been used to study the polymorphism in crystals and microcrystals of the organic semiconductor 9,10-diphenylanthracene (DPA) obtained by various methods. While solution grown specimens all display the well-known monoclinic structure widely reported in the literature, by varying the growth conditions two more polymorphs have been obtained, either from the melt or by sublimation. By injecting water as a nonsolvent in a DPA solution, one of the two new polymorphs was predominantly obtained in the shape of microribbons. Lattice energy calculations allow us to assess the relative thermodynamic stability of the polymorphs and verify that the energies of the different phases are very sensitive to the details of the molecular geometry adopted in the solid state. The mobility channels of DPA polymorphs are shortly investigated.},
author={Salzillo, Tommaso and {Della Valle}, Raffaele Guido and Venuti, Elisabetta and Brillante, Aldo and Siegrist, Theo and Masino, Matteo and Mezzadri, Francesco and Girlando, Alberto},
doi={10.1021/acs.jpcc.5b11115},
file={:home/purcell/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Salzillo et al. - 2016 - Two New Polymorphs of the Organic Semiconductor 9,10-Diphenylanthracene Raman and X-ray Analysis.pdf:pdf},
issn={19327455},
journal={Journal of Physical Chemistry C},
number={3},
pages={1831--1840},
publisher={UTC},
title={{Two new polymorphs of the organic semiconductor 9,10-diphenylanthracene: Raman and X-ray analysis}},
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Author="Gonze, Xavier and Amadon, Bernard and Antonius, Gabriel and Arnardi, Frédéric and Baguet, Lucas and Beuken, Jean-Michel and Bieder, Jordan and Bottin, François and Bouchet, Johann and Bousquet, Eric and Brouwer, Nils and Bruneval, Fabien and Brunin, Guillaume and Cavignac, Théo and Charraud, Jean-Baptiste and Chen, Wei and Côté, Michel and Cottenier, Stefaan and Denier, Jules and Geneste, Grégory and Ghosez, Philippe and Giantomassi, Matteo and Gillet, Yannick and Gingras, Olivier and Hamann, Donald R. and Hautier, Geoffroy and He, Xu and Helbig, Nicole and Holzwarth, Natalie and Jia, Yongchao and Jollet, François and Lafargue-Dit-Hauret, William and Lejaeghere, Kurt and Marques, Miguel A. L. and Martin, Alexandre and Martins, Cyril and Miranda, Henrique P. C. and Naccarato, Francesco and Persson, Kristin and Petretto, Guido and Planes, Valentin and Pouillon, Yann and Prokhorenko, Sergei and Ricci, Fabio and Rignanese, Gian-Marco and Romero, Aldo H. and Schmitt, Michael Marcus and Torrent, Marc and van Setten, Michiel J. and Troeye, Benoit Van and Verstraete, Matthieu J. and Zérah, Gilles and Zwanziger, Josef W.",
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@misc{AiiDA,
Author={Sebastiaan. P. Huber and Spyros Zoupanos and Martin Uhrin and Leopold Talirz and Leonid Kahle and Rico Häuselmann and Dominik Gresch and Tiziano Müller and Aliaksandr V. Yakutovich and Casper W. Andersen and Francisco F. Ramirez and Carl S. Adorf and Fernando Gargiulo and Snehal Kumbhar and Elsa Passaro and Conrad Johnston and Andrius Merkys and Andrea Cepellotti and Nicolas Mounet and Nicola Marzari and Boris Kozinsky and Giovanni Pizzi},
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@article{Ouyang2017,
abstract={The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator-{\textgreater}metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.},
archivePrefix={arXiv},
arxivId={1710.03319},
author={Ouyang, Runhai and Curtarolo, Stefano and Ahmetcik, Emre and Scheffler, Matthias and Ghiringhelli, Luca M},
doi={10.1103/PhysRevMaterials.2.083802},
eprint={1710.03319},
file={:home/purcell/Documents/Mendeley Desktop/Ouyang et al. - Physical Review Materials - 2018.pdf:pdf},
issn={2475-9953},
journal={Phys. Rev. Mater.},
month={aug},
number={8},
pages={083802},
title={{SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates}},
Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
The SISSO++ package is a C++ implementation of the sure-independence screening and sparsifying operator (SISSO) method with python bindings.
SISSO is a symbolic regression method that takes in a set of input primary features and iteratively applies a set of analytical unary and binary operators to build a large and exhaustive feature space.
SISSO is a symbolic regression method that takes in a set of input primary features and iteratively applies a set of analytical unary and binary operators to build a large and exhaustive feature space [@Ouyang2019a, @Ouyang2017].
From here, an $\ell_0$-regularization is performed to find the best low-dimensional linear model of the features using the SISSO operator.
Specifically, SISSO++ applies this methodology for regression, log regression, and classification problems using separate loss functions.
The package uses standard input file formats to allow for an accessible command line interface as well as supporting python interface to the underlying C++ objects.
