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2023

Articles

1. J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
   Interpretable Machine Learning for Materials Design.
   Journal of Materials Research (2023); https://doi.org/10.1557/s43578-023-01164-w 
   Download: pdf

2. L. Foppa, F. Rüther, M. Geske, G. Koch, F. Girgsdies, P. Kube, S. J. Carey, M. Hävecker, O. Timpe, A. V. Tarasov, M. Scheffler, F. Rosowski, R. Schlögl, and A. Trunschke,
   Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation.
   J. Am. Chem. Soc. 2023, 145, 6, 3427–3442; https://doi.org/10.1021/jacs.2c11117
   Download: pdf

3. L. M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. T. Koch, M. Kühbach, A. N. Ladines, P. Lambrix, M.-O. Lenz-Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G.-M. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, and M. Scheffler,
   Shared Metadata for Data-Centric Materials Science.
   Scientific Data 10, 626 (2023); https://doi.org/10.1038/s41597-023-02501-8
   Download: pdf

4. H. Lu, G. Koknat, Y. Yao, J. Hao, X. Qin, C. Xiao, R. Song, F. Merz, M. Rampp, S. Kokott, C. Carbogno, T. Li, G. Teeter, M. Scheffler, J. J. Berry, D. B. Mitzi, J. L. Blackburn, V. Blum, and M. C. Beard,
   Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn.
   PRX Energy 2, 023010 (2023); http://dx.doi.org/10.1103/PRXEnergy.2.023010  
   Download: pdf

5. F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno,
   Anharmonicity in Thermal Insulators – An Analysis from First Principles.
   Phys. Rev. Lett. 130, 236301 (2023); https://doi.org/10.1103/PhysRevLett.130.236301
   Download: pdf

6. F. Knoop, M. Scheffler, and C. Carbogno,
   Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
   Phys. Rev. B 107, 224304 (2023); https://doi.org/10.1103/PhysRevB.107.224304
   Download: pdf

7. M. F. Langer, F. Knoop. C. Carbogno, M. Scheffler, and M. Rupp, 
   Heat flux for semi-local machine-learning potentials.
   Phys. Rev. B (Letter) 108, L100302 (2023); https://doi.org/10.1103/PhysRevB.108.L100302
   Download: pdf

8. M. F. Langer, J. T. Frank, F. Knoop,
   Stress and heat flux via automatic differentiation.
   J. Chem. Phys. 159, 174105 (2023); https://doi.org/10.1063/5.0155760 
   Download: pdf

9. A. Leitherer, B. C. Yeo, C. H. Liebscher, and L. M. Ghiringhelli,
   Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy.
   npj Computational Materials 9, 17 (2023); https://doi.org/10.1038/s41524-023-01133-1
   Download: pdf

10. M. Scheidgen, L. Himanen, A. N. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. A. Márquez, S. Brockhauser, S. Brückner, L. M. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, P. Ondračka, H.-J. Bungartz, and C. Draxl,
    NOMAD: A distributed web-based platform for managing materials science research data.
    Journal of Open Source Software, 8 (90), 5388; https://doi.org/10.21105/joss.05388
    Download: pdf

11. O. T. Beynon, A. Owens, C. Carbogno, and A. J. Logsdail,
    Evaluating the Role of Anharmonic Vibrations in Zeolite β Materials.
    J. Phys. Chem. C 127, 16030 (2023); https://doi.org/10.1021/acs.jpcc.3c02863
    Download: pdf 

12. S. Lu, L. M. Ghiringhelli, C. Carbogno, J. Wang, M. Scheffler,
    On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials.
    September 1, 2023; https://doi.org/10.48550/arXiv.2309.00195
    Preprint Download (2023): arXiv

13. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli, C. Carbogno,
    Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity.
    npj Computational Materials 9 (1), 112 (2023); https://doi.org/10.1038/s41524-023-01063-y
    Download: pdf

14. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli,
    Recent advances in the SISSO method and their implementation in the SISSO++ code.
    J. Chem. Phys. 159, 114110 (2023); https://doi.org/10.1063/5.0156620
    Download (2023): pdf

2023

Ph.D. Thesis

1. M. F. Langer, 
   Machine Learning for Atomistic Modeling: Representations and Thermal Transport.
   TU Berlin, 2023; https://doi.org/10.14279/depositonce-18647
   Download: pdf

2. S. Bi, 
   Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework.
   HU Berlin, 2023; https://doi.org/10.18452/26094
   Download: pdf

2023

Master Thesis

1. F. Fiebig,
   Assessing Electronic Transport in Solid Materials via the Fluctuation-Dissipation Theorem.
   TU Berlin, 2023;
   Download: pdf