| The origin of ferroelectricity in Hf1− xZrxO2: A computational investigation and a surface energy model R Materlik, C Künneth, A Kersch Journal of Applied Physics 117 (13), 2015 | 807 | 2015 |
| Ferroelectric phase transitions in nanoscale HfO2 films enable giant pyroelectric energy conversion and highly efficient supercapacitors M Hoffmann, U Schroeder, C Künneth, A Kersch, S Starschich, U Böttger, ... Nano Energy 18, 154-164, 2015 | 234 | 2015 |
| Polymer informatics: Current status and critical next steps L Chen, G Pilania, R Batra, TD Huan, C Kim, C Kuenneth, R Ramprasad Materials Science and Engineering: R: Reports 144, 100595, 2021 | 196 | 2021 |
| Thermodynamics of Phase Stability and Ferroelectricity From First Principles C Künneth, R Batra, GAJ Rossetti, R Ramprasad, A Kersch Ferroelectricity in Doped Hafnium Oxide: Materials, Properties and Devices …, 2019 | 165* | 2019 |
| Evidence of single domain switching in hafnium oxide based FeFETs: Enabler for multi-level FeFET memory cells H Mulaosmanovic, S Slesazeck, J Ocker, M Pesic, S Muller, S Flachowsky, ... 2015 IEEE International Electron Devices Meeting (IEDM), 26.8. 1-26.8. 3, 2015 | 155 | 2015 |
| Domain pinning: Comparison of hafnia and PZT based ferroelectrics FPG Fengler, M Pešić, S Starschich, T Schneller, C Künneth, U Böttger, ... Advanced Electronic Materials 3 (4), 1600505, 2017 | 130 | 2017 |
| Al-, Y-, and La-doping effects favoring intrinsic and field induced ferroelectricity in HfO2: A first principles study R Materlik, C Künneth, M Falkowski, T Mikolajick, A Kersch Journal of Applied Physics 123 (16), 2018 | 126 | 2018 |
| On the Origin of the Large Remanent Polarization in La:HfO2 T Schenk, CM Fancher, MH Park, C Richter, C Künneth, A Kersch, ... Advanced Electronic Materials 5 (12), 1900303, 2019 | 112 | 2019 |
| Polymer informatics with multi-task learning C Kuenneth, AC Rajan, H Tran, L Chen, C Kim, R Ramprasad Patterns 2 (4), 100238, 2021 | 89 | 2021 |
| Impact of Four-Valent Doping on the Crystallographic Phase Formation for Ferroelectric HfO2 from First-Principles: Implications for Ferroelectric Memory and … C Künneth, R Materlik, M Falkowski, A Kersch ACS Applied Nano Materials 1 (1), 254-264, 2017 | 81 | 2017 |
| Modeling ferroelectric film properties and size effects from tetragonal interlayer in Hf1–xZrxO2 grains C Künneth, R Materlik, A Kersch Journal of Applied Physics 121 (20), 2017 | 66 | 2017 |
| polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics C Kuenneth, R Ramprasad Nature Communications 14 (1), 4099, 2023 | 65 | 2023 |
| Copolymer informatics with multitask deep neural networks C Kuenneth, W Schertzer, R Ramprasad Macromolecules 54 (13), 5957-5961, 2021 | 64 | 2021 |
| A computational study of hafnia-based ferroelectric memories: From ab initio via physical modeling to circuit models of ferroelectric device M Pešić, C Künneth, M Hoffmann, H Mulaosmanovic, S Müller, ET Breyer, ... Journal of computational Electronics 16, 1236-1256, 2017 | 57 | 2017 |
| A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing P Shetty, AC Rajan, C Kuenneth, S Gupta, LP Panchumarti, L Holm, ... npj Computational Materials 9 (1), 52, 2023 | 48* | 2023 |
| The impact of charge compensated and uncompensated strontium defects on the stabilization of the ferroelectric phase in HfO2 R Materlik, C Künneth, T Mikolajick, A Kersch Applied Physics Letters 111 (8), 2017 | 40 | 2017 |
| Polymer informatics at scale with multitask graph neural networks R Gurnani, C Kuenneth, A Toland, R Ramprasad Chemistry of Materials 35 (4), 1560-1567, 2023 | 34 | 2023 |
| Bioplastic design using multitask deep neural networks C Kuenneth, J Lalonde, BL Marrone, CN Iverson, R Ramprasad, G Pilania Communications Materials 3 (1), 96, 2022 | 27 | 2022 |
| Unexpectedly large energy variations from dopant interactions in ferroelectric HfO2 from high-throughput ab initio calculations M Falkowski, C Künneth, R Materlik, A Kersch npj Computational Materials 4 (1), 73, 2018 | 26 | 2018 |
| An efficient deep learning scheme to predict the electronic structure of materials and molecules: The example of graphene-derived allotropes BG Del Rio, C Kuenneth, HD Tran, R Ramprasad The Journal of Physical Chemistry A 124 (45), 9496-9502, 2020 | 23 | 2020 |
