Active learning machine learns to create new quantum experiments. - Alexey A. Melnikov 001; Hendrik Poulsen Nautrup; Mario Krenn; Vedran Dunjko; Markus Tiersch; Anton Zeilinger; Hans J. Briegel (CoRR, 2017)

Quantifying high dimensional entanglement with two mutually unbiased bases. - Paul Erker; Mario Krenn; Marcus Huber (Quantum, 2017)

SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry. - Mario Krenn; Florian Häse; AkshatKumar Nigam; Pascal Friederich; Alán Aspuru-Guzik (CoRR, 2019)

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics. - Mario Krenn; Anton Zeilinger (CoRR, 2019)

Quantum Optical Experiments Modeled by Long Short-Term Memory. - Thomas Adler; Manuel Erhard; Mario Krenn; Johannes Brandstetter; Johannes Kofler; Sepp Hochreiter (CoRR, 2019)

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space. - AkshatKumar Nigam; Pascal Friederich; Mario Krenn; Alán Aspuru-Guzik (ICLR, 2020)

Computer-inspired Quantum Experiments. - Mario Krenn; Manuel Erhard; Anton Zeilinger (CoRR, 2020)

Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning. - Luca A. Thiede; Mario Krenn; AkshatKumar Nigam; Alán Aspuru-Guzik (CoRR, 2020)

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation. - Mario Krenn; Florian Häse; AkshatKumar Nigam; Pascal Friederich; Alán Aspuru-Guzik (Mach. Learn. Sci. Technol., 2020)

Scientific intuition inspired by machine learning-generated hypotheses. - Pascal Friederich; Mario Krenn; Isaac Tamblyn; Alán Aspuru-Guzik (Mach. Learn. Sci. Technol., 2021)

Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations. - Cynthia Shen; Mario Krenn; Sagi Eppel; Alán Aspuru-Guzik (Mach. Learn. Sci. Technol., 2021)

On scientific understanding with artificial intelligence. - Mario Krenn; Robert Pollice; Si Yue Guo; Matteo Aldeghi; Alba Cervera-Lierta; Pascal Friederich; Gabriel dos Passos Gomes; Florian Häse; Adrian Jinich; AkshatKumar Nigam; Zhenpeng Yao; Alán Aspuru-Guzik (CoRR, 2022)

Artificial Intelligence and Machine Learning for Quantum Technologies. - Mario Krenn; Jonas Landgraf; Thomas Foesel; Florian Marquardt (CoRR, 2022)

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network. - Mario Krenn; Lorenzo Buffoni; Bruno C. Coutinho; Sagi Eppel; Jacob Gates Foster; Andrew Gritsevskiy; Harlin Lee; Yichao Lu; João P. Moutinho; Nima Sanjabi; Rishi Sonthalia; Ngoc Mai Tran; et al. (CoRR, 2022)

Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning. - Luca A. Thiede; Mario Krenn; AkshatKumar Nigam; Alán Aspuru-Guzik (Mach. Learn. Sci. Technol., 2022)

Learning interpretable representations of entanglement in quantum optics experiments using deep generative models. - Daniel Flam-Shepherd; Tony C. Wu; Xuemei Gu; Alba Cervera-Lierta; Mario Krenn; Alán Aspuru-Guzik (Nat. Mach. Intell., 2022)

SELFIES and the future of molecular string representations. - Mario Krenn; Qianxiang Ai; Senja Barthel; Nessa Carson; Angelo Frei; Nathan C. Frey; Pascal Friederich; Théophile Gaudin; Alberto Alexander Gayle; Kevin Maik Jablonka; Rafael F. Lameiro; Dominik Lemm; et al. (Patterns, 2022)

Design of quantum optical experiments with logic artificial intelligence. - Alba Cervera-Lierta; Mario Krenn; Alán Aspuru-Guzik (Quantum, 2022)

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library. - Alston Lo; Robert Pollice; AkshatKumar Nigam; Andrew D. White; Mario Krenn; Alán Aspuru-Guzik (CoRR, 2023)

Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. - Mario Krenn; Lorenzo Buffoni; Bruno C. Coutinho; Sagi Eppel; Jacob Gates Foster; Andrew Gritsevskiy; Harlin Lee; Yichao Lu; João P. Moutinho; Nima Sanjabi; Rishi Sonthalia; Ngoc Mai Tran; et al. (Nat. Mac. Intell., 2023)

Digital Discovery of 100 diverse Quantum Experiments with PyTheus. - Carlos Ruiz-Gonzalez; Sören Arlt; Jan Petermann; Sharareh Sayyad; Tareq Jaouni; Ebrahim Karimi; Nora Tischler; Xuemei Gu; Mario Krenn (Quantum, 2023)

Forecasting high-impact research topics via machine learning on evolving knowledge graphs. - Xuemei Gu; Mario Krenn (CoRR, 2024)

Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models. - Xuemei Gu; Mario Krenn (CoRR, 2024)

Meta-Designing Quantum Experiments with Language Models. - Sören Arlt; Haonan Duan 002; Felix Li; Sang Michael Xie; Yuhuai Wu; Mario Krenn (CoRR, 2024)

Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics. - Philipp Schmidt; Sören Arlt; Carlos Ruiz-Gonzalez; Xuemei Gu; Carla Rodríguez; Mario Krenn (Mach. Learn. Sci. Technol., 2024)

Deep quantum graph dreaming: deciphering neural network insights into quantum experiments. - Tareq Jaouni; Sören Arlt; Carlos Ruiz-Gonzalez; Ebrahim Karimi; Xuemei Gu; Mario Krenn (Mach. Learn. Sci. Technol., 2024)

Discovering emergent connections in quantum physics research via dynamic word embeddings. - Felix Frohnert; Xuemei Gu; Mario Krenn; Evert P. L. van Nieuwenburg (Mach. Learn. Sci. Technol., 2025)

Quantum computing and artificial intelligence: status and perspectives. - Giovanni Acampora; Andris Ambainis; Natalia Ares; Leonardo Banchi; Pallavi Bhardwaj; Daniele Binosi; G. Andrew D. Briggs; Tommaso Calarco; Vedran Dunjko; Jens Eisert; Olivier Ezratty; Paul Erker; et al. (CoRR, 2025)