SchNet – A deep learning architecture for molecules and materials von Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., Müller, K.-R.

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Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions von Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R., Maurer, R. J.

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SchNetPack: A Deep Learning Toolbox For Atomistic Systems von Schütt, K. T, Kessel, P, Gastegger, M, Nicoli, K. A, Tkatchenko, A, Müller, K.-R

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DFTB+, a software package for efficient approximate density functional theory based atomistic simulations von Hourahine, B., Aradi, B., Blum, V., Bonafé, F., Buccheri, A., Camacho, C., Cevallos, C., Deshaye, M. Y., Dumitrică, T., Dominguez, A., Ehlert, S., Elstner, M., van der Heide, T., Hermann, J., Irle, S., Kranz, J. J., Köhler, C., Kowalczyk, T., Kubař, T., Lee, I. S., Lutsker, V., Maurer, R. J., Min, S. K., Mitchell, I., Negre, C., Niehaus, T. A., Niklasson, A. M. N., Page, A. J., Pecchia, A., Penazzi, G., Persson, M. P., Řezáč, J., Sánchez, C. G., Sternberg, M., Stöhr, M., Stuckenberg, F., Tkatchenko, A., Yu, V. W.-z., Frauenheim, T.

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Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark von Slootman, E., Poltavsky, I., Shinde, R., Cocomello, J., Moroni, S., Tkatchenko, A., Filippi, C.

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Comment on “Dispersion Interaction between Two Hydrogen Atoms in a Static Electric Field” von Karimpour, M. R., Fedorov, D. V., Tkatchenko, A.

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Roadmap on Machine learning in electronic structure von Kulik, H J, Hammerschmidt, T, Schmidt, J, Botti, S, Marques, M A L, Boley, M, Scheffler, M, Todorović, M, Rinke, P, Oses, C, Smolyanyuk, A, Curtarolo, S, Tkatchenko, A, Bartók, A P, Manzhos, S, Ihara, M, Carrington, T, Behler, J, Isayev, O, Veit, M, Grisafi, A, Nigam, J, Ceriotti, M, Schütt, K T, Westermayr, J, Gastegger, M, Maurer, R J, Kalita, B, Burke, K, Nagai, R, Akashi, R, Sugino, O, Hermann, J, Noé, F, Pilati, S, Draxl, C, Kuban, M, Rigamonti, S, Scheidgen, M, Esters, M, Hicks, D, Toher, C, Balachandran, P V, Tamblyn, I, Whitelam, S, Bellinger, C, Ghiringhelli, L M

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Erratum: “DFTB+, a software package for efficient approximate density functional theory based atomistic simulations” [J. Chem. Phys. 152, 124101 (2020)] von Hourahine, B., Aradi, B., Blum, V., Bonafé, F., Buccheri, A., Camacho, C., Cevallos, C., Deshaye, M. Y., Dumitrică, T., Dominguez, A., Ehlert, S., Elstner, M., van der Heide, T., Hermann, J., Irle, S., Jakowski, J., Kranz, J. J., Köhler, C., Kowalczyk, T., Kubař, T., Lee, I. S., Lutsker, V., Maurer, R. J., Min, S. K., Mitchell, I., Negre, C., Niehaus, T. A., Niklasson, A. M. N., Page, A. J., Pecchia, A., Penazzi, G., Persson, M. P., Řezáč, J., Sánchez, C. G., Sternberg, M., Stöhr, M., Stuckenberg, F., Tkatchenko, A., Yu, V. W.-z., Frauenheim, T.

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Comparative Analysis of Indicators of the Early Stages of Sensory and Sensorimotor Information Processing in Aggressive and Nonaggressive Schizophrenia Patients von Kirenskaya, A. V., Storozheva, Z. I., Ilyushina, E. A., Tkatchenko, A. A.

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Wavelike charge density fluctuations and van der Waals interactions at the nanoscale von Ambrosetti, Alberto, Ferri, Nicola, DiStasio, Robert A., Tkatchenko, Alexandre

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Message Passing Neural Networks von Gilmer, Justin, Schoenholz, Samuel S., Riley, Patrick F., Vinyals, Oriol, Dahl, George E.

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Generative Models for Automatic Chemical Design von Schwalbe-Koda, Daniel, Gómez-Bombarelli, Rafael

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Interference of Lithium in Measuring Magnesium by Complexometry: Discussions of the Mechanism von Gao, Jie, Guo, Yafei, Wang, Shiqiang, Deng, Tianlong, Chen, Yu-Wei, Belzile, Nelson

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Overexpression of Hoxc13 in differentiating keratinocytes results in downregulation of a novel hair keratin gene cluster and alopecia von Tkatchenko, A V, Visconti, R P, Shang, L, Papenbrock, T, Pruett, N D, Ito, T, Ogawa, M, Awgulewitsch, A

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Sustainability Assessment of Chemical Processes: Evaluation of Three Synthesis Routes of DMC von Pongrácz, Eva, Yadav, Ganapati D., Oravisjärvi, Kati, Turpeinen, Esa, Kabra, Satish, Saavalainen, Paula, Keiski, Riitta L.

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Collective many-body van der Waals interactions in molecular systems von DiStasio, Robert A., von Lilienfeld, O. Anatole, Tkatchenko, Alexandre

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Molecular Dynamics with Neural Network Potentials von Gastegger, Michael, Marquetand, Philipp

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DFTB plus , a software package for efficient approximate density functional theory based atomistic simulations von Hourahine, B., Aradi, B., Blum, V., Bonafe, F., Buccheri, A., Camacho, C., Cevallos, C., Deshaye, M. Y., Dumitrica, T., Dominguez, A., Ehlert, S., Elstner, M., van der Heide, T., Hermann, J., Irle, S., Kranz, J. J., Koehler, C., Kowalczyk, T., Kubar, T., Lee, I. S., Lutsker, V., Maurer, R. J., Min, S. K., Mitchell, I., Negre, C., Niehaus, T. A., Niklasson, A. M. N., Page, A. J., Pecchia, A., Penazzi, G., Persson, M. P., Rezac, J., Sanchez, C. G., Sternberg, M., Stoehr, M., Stuckenberg, F., Tkatchenko, A., Yu, V. W. -z., Frauenheim, T.

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Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design von Armiento, Rickard

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Synthesis of colloidal ruthenium nanocatalyst by chemical reduction method von Patharkar, R. G., Nandanwar, S. U., Chakraborty, M.

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