Journal Article (93)

2023
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Katnagallu, S.; Freysoldt, C.; Gault, B.; Neugebauer, J.: Ab initio vacancy formation energies and kinetics at metal surfaces under high electric field. Physical Review B 107 (4), L041406 (2023)
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Kaygisiz, K.; Dutta, A.; Rauch-Wirth, L.; Synatschke, C. V.; Münch, J.; Bereau, T.; Weil, T.: Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings. Biomaterials Science 11 (15), pp. 5251 - 5261 (2023)
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Kaygisiz, K.; Rauch-Wirth, L.; Dutta, A.; Yu, X.; Nagata, Y.; Bereau, T.; Münch, J.; Synatschke, C. V.; Weil, T.: Data-mining unveils structure-property-activity correlation of viral infectivity enhancing self-assembling peptides. Nature Communications 14, 5121 (2023)
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Khorrami, M. S.; Mianroodi, J. R.; Siboni, N. H.; Goyal, P. K.; Svendsen, B.; Benner, P.; Raabe, D.: An Artificial Neural Network for Surrogate Modeling of Stress Fields in Viscoplastic Polycrystalline Materials. npj Computational Materials 9, 37 (2023)
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Knoop, F.; Purcell, T. A. R.; Scheffler, M.; Carbogno, C.: Anharmonicity in Thermal Insulators: An Analysis from First Principles. Physical Review Letters 130 (23), 236301 (2023)
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Kour, K.; Dolgov, S.; Stoll, M.; Benner, P.: Efficient Structure-preserving Support Tensor Train Machine. Journal of Machine Learning Research 24 (4), pp. 1 - 22 (2023)
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Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. International Journal of Plasticity 171, 103776 (2023)
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Leitherer, A.; Yeo, B. C.; Liebscher, C. H.; Ghiringhelli, L. M.: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy. npj Computational Materials 9 (1), 179 (2023)
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Li, Y.; Wei, Y.; Wang, Z.; Liu, X.; Colnaghi, T.; Han, L.; Rao, Z.; Zhou, X.; Huber, L.; Dsouza, R. et al.; Gong, Y.; Neugebauer, J.; Marek, A.; Rampp, M.; Bauer, S.; Li, H.; Baker, I.; Stephenson, L.; Gault, B.: Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. Nature Communications 14 (1), 7410 (2023)
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Pincelli, T.; Vasileiadis, T.; Dong, S.; Beaulieu, S.; Dendzik, M. R.; Zahn, D.; Lee, S.-E.; Seiler, H.; Qi, Y.; Xian, R. P. et al.; Maklar, J.; Coy, E.; Mueller, N. S.; Okamura, Y.; Reich, S.; Wolf, M.; Rettig, L.; Ernstorfer, R.: Observation of Multi-Directional Energy Transfer in a Hybrid Plasmonic–Excitonic Nanostructure. Advanced Materials 35 (9), 2209100 (2023)
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Purcell, T. A.R.; Scheffler, M.; Ghiringhelli, L. M.: Recent advances in the SISSO method and their implementation in the SISSO plus plus code. The Journal of Chemical Physis 159, 114110 (2023)
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Rao, Z.; Li, Y.; Zhang, H.; Colnaghi, T.; Marek, A.; Rampp, M.; Gault, B.: Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms. Scripta Materialia 234, 115542 (2023)
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Xian, R. P.; Stimper, V.; Zacharias, M.; Dendzik, M. R.; Dong, S.; Beaulieu, S.; Schölkopf, B.; Wolf, M.; Rettig, L.; Carbogno, C. et al.; Bauer, S.; Ernstorfer, R.: A machine learning route between band mapping and band structure. Nature Computational Science 3 (1), pp. 101 - 114 (2023)
2022
Journal Article
Benavides-Riveros, C. L.; Chen, L.; Schilling, C.; Mantilla, S.; Pitallis, S.: Excitations of Quantum Many-Body Systems via Purified Ensembles: A Unitary-Coupled-Cluster-Based Approach. Physical Review Letters 129 (6), 066401 (2022)
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Bowker, M.; DeBeer, S.; Dummer, N. F.; Hutchings, G. J.; Scheffler, M.; Schüth, F.; Taylor, S. H.; Tüysüz, H.: Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angewandte Chemie International Edition 61 (50), e202209016 (2022)
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Cautaerts, N.; Crout, P.; Ånes, H. W.; Prestat, E.; Jeong, J.; Dehm, G.; Liebscher, C.: Free, flexible and fast: Orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the Python-based open source 4D-STEM analysis toolbox Pyxem. Ultramicroscopy 237, 113517 (2022)
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Dutta, A.; Bereau, T.; Vilgis, T. A.: Identifying Sequential Residue Patterns in Bitter and Umami Peptides. ACS Food Science & Technology 2 (11), pp. 1773 - 1780 (2022)
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Gedeon, J.; Schmidt, J.; Hodgson, M. J. P.; Wetherell, J.; Benavides-Riveros, C. L.; Marques, M. A. L.: Machine learning the derivative discontinuity of density-functional theory. Machine Learning: Science and Technology 3 (1), 015011 (2022)
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Goyal, P. K.; Benner, P.: Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach. Proceedings of the Royal Society A 478 (2262), 20210883 (2022)
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Kühbach, M. T.; London, A. J.; Wang, J.; Schreiber, D. K.; Mendez Martin, F.; Ghamarian, I.; Bilal, H.; Ceguerra, A. V.: Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis 28 (4), pp. 1038 - 1053 (2022)
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Miyazaki, R.; Iida, K.; Ohno, S.; Matsuzaki, T.; Suzuki, T.; Arisawa, M.; Hasegawa, J.-y.: Substrate-Assisted Reductive Elimination Determining the Catalytic Cycle: A Theoretical Study on the Ni-Catalyzed 2,3-Disubstituted Benzofuran Synthesis via C-O Bond Activation. Organometallics 41 (23), pp. 3581 - 3588 (2022)
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Peivaste, I.; Hamidi Siboni, N.; Alahyarizadeh, G.; Ghaderi, R.; Svendsen, B.; Raabe, D.; Mianroodi, J. R.: Machine-learning-based surrogate modeling of microstructure evolution using phase-field. Computational Materials Science 214, 111750 (2022)
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Peng, Z.; Meiners, T.; Lu, Y.; Liebscher, C.; Kostka, A.; Raabe, D.; Gault, B.: Quantitative analysis of grain boundary diffusion, segregation and precipitation at a sub-nanometer scale. Acta Materialia 225, 117522 (2022)
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Regler, B.; Scheffler, M.; Ghiringhelli, L. M.: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Mining and Knowledge Discovery (5), pp. 1815 - 1864 (2022)
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Sasidhar, K. N.; Hamidi Siboni, N.; Mianroodi, J. R.; Rohwerder, M.; Neugebauer, J.; Raabe, D.: Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys. npj Materials Degradation 6 (1), 71 (2022)
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