Machine Learning Network at NCBJ
Catalysis+ is an interdisciplinary research network initiated by scientists Dr. Amil Aligayev and Dr. Javier Dominguez at NOMATEN CoE in the National Centre for Nuclear Research (NCBJ), dedicated to advancing the science and application of catalysis. Our mission is to accelerate breakthroughs in catalysis by building a dynamic, collaborative network that bridges fundamental research and practical application. We aim to unite researchers across disciplines and institutions to address key scientific and technological challenges in catalysis, promote sustainable solutions, and foster the next generation of experts through shared knowledge, resources, and innovation. The network serves as a platform for joint projects, knowledge exchange, and innovation in energy, materials, and environmental applications.
- DBP /BP4
large scale structure of the Universe, galaxy evolution, astrostatistics
Various approaches; supervised/unsupervised
whatever enjoyable is good
- Solarz, A., Pollo, A., Takeuchi, T. T. et al., Star-galaxy separation in the AKARI NEP deep field, Astronomy & Astrophysics, Volume 541, id.A50, 8 pp, 2012, https://www.aanda.org/articles/aa/pdf/2012/05/aa18108-11.pdf
Małek, K., Solarz, A., Pollo, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). A support vector machine classification of galaxies, stars, and AGNs”, Astronomy & Astrophysics, Volume 557, id.A16, 16 pp., 2013, https://www.aanda.org/articles/aa/full_html/2013/09/aa21447-13/aa21447-13.html
Kurcz, A.; Bilicki, M.; Solarz, A.; Krupa, M.; Pollo, A.; Małek, K., Towards automatic classification of all WISE sources, Astronomy & Astrophysics, Volume 592, id.A25, 18 pp., 2016, https://www.aanda.org/articles/aa/pdf/2016/08/aa28142-16.pdf
Krakowski, T.; Małek, K.; Bilicki, M.; Pollo, A.; Kurcz, A.; Krupa, M., “Machine-learning identification of galaxies in the WISE × SuperCOSMOS all-sky catalogue”, Astronomy & Astrophysics, Volume 596, id.A39, 11 pp., 2016, https://ui.adsabs.harvard.edu/link_gateway/2016A%26A...596A..39K/PUB_PDF
Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M., Automated novelty detection in the WISE survey with one-class support vector machines, Astronomy & Astrophysics, Volume 606, id.A39, 13 pp., 2017, https://www.aanda.org/articles/aa/pdf/2017/10/aa30968-17.pdf
Siudek, M.; Małek, K.; Pollo, A.; Krakowski, T.; Iovino, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms”, Astronomy & Astrophysics, Volume 617, id.A70, 25 pp., 2018, https://ui.adsabs.harvard.edu/link_gateway/2018A%26A...617A..70S/PUB_PDF
Nakoneczny, S.; Bilicki, M.; Solarz, A.; Pollo, A.; Maddox, N.; Spiniello, C.; Brescia, M.; Napolitano, N. R., Catalog of quasars from the Kilo-Degree Survey Data Release 3, Astronomy & Astrophysics, Volume 624, id.A13, 15 pp., 2019, https://www.aanda.org/articles/aa/pdf/2019/04/aa34794-18.pdf
Poliszczuk, A.; Solarz, A.; Pollo, A.; Bilicki, M. et al., Active galactic nucleus selection in the AKARI NEP-Deep field with the fuzzy support vector machine algorithm, Publications of the Astronomical Society of Japan, Volume 71, Issue 3, id.65, https://arxiv.org/pdf/1902.04922.pdf
Turner, S.; Siudek, M.; Salim, S.; Baldry, I. K.; Pollo, A.; Longmore, S. N.; Małek, K. et al.; Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning, Monthly Notices of the Royal Astronomical Society, 2021 in press, https://arxiv.org/pdf/2102.05056.pdf
Nakoneczny, S. J.; Bilicki, M.; Pollo, A.; Asgari, M., et al., Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4, Astronomy & Astrophysics, , in press, 2021, https://arxiv.org/pdf/2010.13857.pdf
- Nomaten /
Numerical modeling of nanoindentation of single crystal materials and high entropy metal alloys. Radiation in materials at extreme.
Supervised learning with linear regression, Gaussian Approximation Potential Framework.
- F. J. Dominguez-Gutierrez, J. Byggmaestar, K. Nordlund et al. “On the classification and quantification of crystal defects after energetic bombardment by machine learning molecular dynamics simulations”, Nucl. Mater. and Energy 22, 100724 (2020)
- F. J. Dominguez-Gutierrez, J. Byggmaestar, K. Nordlund et al. “Computational study of crystal defects formation in Mo by machine learned molecular dynamics simulations''. Modelling Simul. Mater. Sci. Eng. (2021) . https://doi.org/10.1088/1361-651X/abf152
- Nomaten /
Statistical and Computational Physics, Plastic flow, Brittle Fracture, Rheology of Yield Stress Fluids, Statistical Seismology, Stochastic Modeling of Earthquakes
computational/statistical libraries in Python
Scikit-learn for linear (multivariate) regression, classification, and cluster analysis
Pandas for data management,
Numpy/Scipy for linear algebra and optimization problems
- Karimi, Kamran, David Amitrano, and Jérôme Weiss. “From plastic flow to brittle fracture: Role of microscopic friction in amorphous solids". Phys. Rev. E, 100:012908, Jul 2019
- Karimi, Kamran and Jean-Louis Barrat. “Correlation and shear bands in a plastically deformed granular medium”. Scientific reports, 8(1):4021, 2018
- Kamran Karimi, Ezequiel E. Ferrero, and Jean-Louis Barrat. “Inertia and universality of avalanche statistics: The case of slowly deformed amorphous solids”. Physical Review E, 95(1):013003, 2017
- DBP /BP4
https://www.ncbj.gov.pl/bp4/dr-hab-katarzyna-malek
https://kasiamalek.weebly.com/
galaxy evolution, dust attenuation
SVM, FEM
- Małek, K., Solarz, A., Pollo, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). A support vector machine classification of galaxies, stars, and AGNs”, Astronomy & Astrophysics, Volume 557, id.A16, 16 pp., 2013, https://www.aanda.org/articles/aa/full_html/2013/09/aa21447-13/aa21447-13.html
- Krakowski, T.; Małek, K.; Bilicki, M.; Pollo, A.; Kurcz, A.; Krupa, M., “Machine-learning identification of galaxies in the WISE × SuperCOSMOS all-sky catalogue”, Astronomy & Astrophysics, Volume 596, id.A39, 11 pp., 2016, https://ui.adsabs.harvard.edu/link_gateway/2016A%26A...596A..39K/PUB_PDF
- Siudek, M.; Małek, K.; Pollo, A.; Krakowski, T.; Iovino, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms”, Astronomy & Astrophysics, Volume 617, id.A70, 25 pp., 2018, https://ui.adsabs.harvard.edu/link_gateway/2018A%26A...617A..70S/PUB_PDF
- NOMATEN /Materials Complexity
Modelling materials, understanding experimental data
Various approaches (SVD, CNN, Random Forests etc.)
enjoys drinking random things at random hours
- Machine learning plastic deformation of crystals, H Salmenjoki, MJ Alava, L Laurson, Nature communications 9 (1), 1-7 (2018).
- Machine learning and predicting the time-dependent dynamics of local yielding in dry foams, L Viitanen, JR Mac Intyre, J Koivisto, A Puisto, M Alava,, Physical Review Research 2 (2), 023338 (2020)
- Probing the transition from dislocation jamming to pinning by machine learning, H Salmenjoki, L Laurson, MJ Alava, Materials Theory 4 (1), 1-16 (2020)
- NOMATEN /Materials Complexity
silviabonfantiphysics.com
Amorphous solids, Mechanical Metamaterials, Biophysics.
CNN, SVM.
- Font-Clos, F., Zanchi, M., Hiemer, S., Bonfanti, S., Guerra, R., Zaiser, M., & Zapperi, S. (2022). Predicting the failure of two-dimensional silica glasses. arXiv preprint arXiv:2201.09723,
- Bonfanti, S., Guerra, R., Font-Clos, F., Rayneau-Kirkhope, D., & Zapperi, S. (2020). Automatic design of mechanical metamaterial actuators. Nature communications, 11(1), 1-10.
- DTJ /TJ2
Galaxy evolution
SVM, FEM
- Krakowski, T.; Małek, K.; Bilicki, M.; Pollo, A.; Kurcz, A.; Krupa, M., “Machine-learning identification of galaxies in the WISE × SuperCOSMOS all-sky catalogue”, Astronomy & Astrophysics, Volume 596, id.A39, 11 pp., 2016, https://ui.adsabs.harvard.edu/link_gateway/2016A%26A...596A..39K/PUB_PDF
Siudek, M.; Małek, K.; Pollo, A.; Krakowski, T.; Iovino, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms”, Astronomy & Astrophysics, Volume 617, id.A70, 25 pp., 2018, https://ui.adsabs.harvard.edu/link_gateway/2018A%26A...617A..70S/PUB_PDF
- DBP/BP4 /
galaxy formation, galaxy evolution, galaxy mergers
Artificial Neural Networks, Convolutional Neural Networks, Auto-Encoders, Generative Adversarial Networks
- “Towards a consistent framework of comparing galaxy mergers in observations and simulations”, Astronomy & Astrophysics, Volume 644, id.A87, 12 pp. 202 https://ui.adsabs.harvard.edu/abs/2020A%26A...644A..87W/abstract
Pearson, W. J.; Wang, L.; Alpaslan, M.; Baldry, I.; Bilicki, M.; Brown, M. J. I.; Grootes, M. W.; Holwerda, B. W.; Kitching, T. D.; Kruk, S.; van der Tak, F. F. S., “Effect of galaxy mergers on star-formation rates”, Astronomy & Astrophysics, Volume 631, id.A51, 19 pp. 2019 https://ui.adsabs.harvard.edu/abs/2019A%26A...631A..51P/abstrac
Pearson, W. J.; Wang, L.; Trayford, J. W.; Petrillo, C. E.; van der Tak, F. F. S., “Identifying galaxy mergers in observations and simulations with deep learning”, Astronomy & Astrophysics, Volume 626, id.A49, 18 pp. 2019 https://ui.adsabs.harvard.edu/abs/2019A%26A...626A..49P/abstract
Pearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F. S., “Deep learning for galaxy mergers in the galaxy main sequence”, Challenges in Panchromatic Modelling with Next Generation Facilities. Proceedings of the International Astronomical Union, Volume 341, pp. 104-108, 2020 https://ui.adsabs.harvard.edu/abs/2020IAUS..341..104P/abstract

