1 code implementation • 7 Feb 2023 • Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.
2 code implementations • 23 Sep 2022 • Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
1 code implementation • 7 Aug 2022 • Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly.
no code implementations • 4 Apr 2022 • 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
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
1 code implementation • 31 Mar 2022 • 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, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew White, Adamo Young, Rose Yu, Alán Aspuru-Guzik
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
1 code implementation • 27 Sep 2021 • Alba Cervera-Lierta, Mario Krenn, Alán Aspuru-Guzik
In this work, we propose the use of logic AI for the design of optical quantum experiments.
1 code implementation • 6 Sep 2021 • Daniel Flam-Shepherd, Tony Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alan Aspuru-Guzik
The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.
no code implementations • 17 Dec 2020 • Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alan Aspuru-Guzik
However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents.
1 code implementation • 17 Dec 2020 • Cynthia Shen, Mario Krenn, Sagi Eppel, Alan Aspuru-Guzik
We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.
no code implementations • 27 Oct 2020 • Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alan Aspuru-Guzik
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas.
2 code implementations • 4 Jun 2020 • Jakob S. Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, Alán Aspuru-Guzik
It is not clear how the full potential of large quantum systems can be exploited.
Quantum Physics Computational Physics Optics
no code implementations • 13 May 2020 • Mario Krenn, Jakob Kottmann, Nora Tischler, Alán Aspuru-Guzik
Here we present Theseus, an efficient algorithm for the design of quantum experiments, which we use to solve several open questions in experimental quantum optics.
Quantum Physics Optics
no code implementations • 23 Feb 2020 • Mario Krenn, Manuel Erhard, Anton Zeilinger
We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future.
no code implementations • 30 Oct 2019 • Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter
In this work, we show that machine learning models can provide significant improvement over random search.
2 code implementations • ICLR 2020 • AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik
Challenges in natural sciences can often be phrased as optimization problems.
1 code implementation • 17 Jun 2019 • Mario Krenn, Anton Zeilinger
We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data.
2 code implementations • 31 May 2019 • Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
no code implementations • 2 Jun 2017 • Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.