Publikationen von Thomas Purcell

Bauer, S., P. Benner, T. Bereau, V. Blum, M. Boley, C. Carbogno, C.R.A. Catlow, G. Dehm, S. Eibl, R. Ernstorfer, A. Fekete, L. Foppa, P. Fratzl, C. Freysoldt, B. Gault, L.M. Ghiringhelli, S.K. Giri, A. Gladyshev, P. Goyal, J. Hattrick-Simpers, L. Kabalan, P. Karpov, M.S. Khorrami, C. Koch, S. Kokott, T. Kosch, I. Kowalec, K. Kremer, A. Leitherer, Y. Li, C.H. Liebscher, A.J. Logsdail, Z. Lu, F. Luong, A. Marek, F. Merz, J.R. Mianroodi, J. Neugebauer, Z. Pei, T. Purcell, D. Raabe, M. Rampp, M. Rossi, J.-M. Rost, J.E. Saal, U. Saalmann, K.N. Sasidhar, A. Saxena, L. Sbailo, M. Scheidgen, M. Schloz, D.F. Schmidt, S. Teshuva, A. Trunschke, Y. Wei, G. Weikum, R.P. Xian, Y. Yao, J. Yin, M. Zhao und M. Scheffler: Roadmap on data-centric materials science. Modelling and Simulation in Materials Science and Engineering 32 (6), 063301 (2024).

Dean, J., M. Scheffler, T. Purcell, S.V. Barabash, R. Bhowmik und T. Bazhirov: Interpretable Machine Learning for Materials Design. Journal of Materials Research 38 (20), 4477–4496 (2023).

Knoop, F., T. Purcell, M. Scheffler und C. Carbogno: Anharmonicity in Thermal Insulators: An Analysis from First Principles. Physical Review Letters 130 (23), 236301 (2023).

Purcell, T., M. Scheffler und L.M. Ghiringhelli: Recent advances in the SISSO method and their implementation in the SISSO++ Code. The Journal of Chemical Physics 159 (11), 114110 (2023).

Purcell, T., M. Scheffler, L.M. Ghiringhelli und C. Carbogno: Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Computational Materials 9, 112 (2023).

Foppa, L., T. Purcell, S.V. Levchenko, M. Scheffler und L.M. Ghiringhelli: Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites. Physical Review Letters 129 (5), 0545301 (2022).

Purcell, T., M. Scheffler, C. Carbogno und L.M. Ghiringhelli: SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparisifying Operator Approach. The Journal of Open Source Software 7 (71), 3960 (2022).

Andersen, C.W., R. Armiento, E. Blokhin, G.J. Conduit, S. Dwaraknath, M.L. Evans, Á. Fekete, A. Gopakumar, S. Gražulis, A. Merkys, F.R. Mohamed, C. Oses, G. Pizzi, G.-M. Rignanese, M. Scheidgen, L. Talirz, C. Toher, D. Winston, R. Aversa, K. Choudhary, P. Colinet, S. Curtarolo, D. Di Stefano, C. Draxl, S. Er, M. Esters, M. Fornari, M. Giantomassi, M. Govoni, G. Hautier, V. Hegde, M.K. Horton, P. Huck, G. Huhs, J. Hummelshøj, A. Kariryaa, B. Kozinsky, S. Kumbhar, M. Liu, N. Marzari, A.J. Morris, A.A. Mostofi, K.A. Persson, G. Petretto, T. Purcell, F. Ricci, F. Rose, M. Scheffler, D. Speckhard, M. Uhrin, A. Vaitkus, P. Villars, D. Waroquiers, C. Wolverton, M. Wu und X. Yang: OPTIMADE, an API for exchanging materials data. Scientific Data 8, 217 (2021).

Knoop, F., T. Purcell, M. Scheffler und C. Carbogno: Anharmonicity measure for materials. Physical Review Materials 4 (8), 083809 (2020).

Knoop, F., T. Purcell, M. Scheffler und C. Carbogno: FHI-vibes: Ab Initio Vibrational Simulations. The Journal of Open Source Software 5 (56), 2671 (2020).

Lenz, M.-O., T. Purcell, D. Hicks, S. Curtarolo, M. Scheffler und C. Carbogno: Parametrically constrained geometry relaxations for high-throughput materials science. npj Computational Materials 5, 123 (2019).

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Kang, K., M. Scheffler, C. Carbogno und T. Purcell: Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials Through Active Learning., im Druck.