Florian Häse

4.4k total citations · 4 hit papers
25 papers, 2.7k citations indexed

About

Florian Häse is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Florian Häse has authored 25 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Materials Chemistry, 9 papers in Molecular Biology and 8 papers in Computational Theory and Mathematics. Recurrent topics in Florian Häse's work include Machine Learning in Materials Science (14 papers), Computational Drug Discovery Methods (6 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (5 papers). Florian Häse is often cited by papers focused on Machine Learning in Materials Science (14 papers), Computational Drug Discovery Methods (6 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (5 papers). Florian Häse collaborates with scholars based in Canada, United States and Germany. Florian Häse's co-authors include Alán Aspuru‐Guzik, Loı̈c M. Roch, Pascal Friederich, Christoph Kreisbeck, Mario Krenn, AkshatKumar Nigam, Jonny Proppe, Matteo Aldeghi, Christine Allen and Zeqing Bao and has published in prestigious journals such as Nucleic Acids Research, Advanced Materials and Nature Communications.

In The Last Decade

Florian Häse

25 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

2020 429 citations Mario Krenn, Florian Häse et al. profile →
Machine-learned potentials for next-generation matter simulations

2021 376 citations Pascal Friederich, Florian Häse et al. Nature Materials profile →
On scientific understanding with artificial intelligence

2022 186 citations Mario Krenn, Robert Pollice et al. Nature Reviews Physics profile →
Machine learning models to accelerate the design of polymeric long-acting injectables

2023 127 citations Pauric Bannigan, Zeqing Bao et al. Nature Communications profile →

Peers

Countries citing papers authored by Florian Häse

Since Specialization

Citations

This map shows the geographic impact of Florian Häse's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Florian Häse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florian Häse more than expected).

Fields of papers citing papers by Florian Häse

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Florian Häse. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Florian Häse. The network helps show where Florian Häse may publish in the future.

Co-authorship network of co-authors of Florian Häse

This figure shows the co-authorship network connecting the top 25 collaborators of Florian Häse. A scholar is included among the top collaborators of Florian Häse based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Florian Häse. Florian Häse is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Machine learning models to accelerate the design of polymeric long-acting injectables breakdown → 2023 ·Nature Communications ·Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru‐Guzik, Christine Allen	127
2	On scientific understanding with artificial intelligence breakdown → 2022 ·Nature Reviews Physics ·Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrián Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru‐Guzik	186
3	Machine-learned potentials for next-generation matter simulations breakdown → 2021 ·Nature Materials ·Pascal Friederich, Florian Häse, Jonny Proppe, Alán Aspuru‐Guzik	376
4	Automated design of superconducting circuits and its application to 4-local couplers 2021 ·npj Quantum Information ·Tim Menke, Florian Häse, Simon Gustavsson, Andrew J. Kerman, William D. Oliver, Alán Aspuru‐Guzik	23
5	Data-science driven autonomous process optimization 2021 ·Communications Chemistry ·Melodie Christensen, Lars P. E. Yunker, (unknown), Florian Häse, Loı̈c M. Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew S. Sigman, Alán Aspuru‐Guzik, Jason E. Hein	143
6	Machine learning directed drug formulation development 2021 ·Advanced Drug Delivery Reviews ·Pauric Bannigan, Matteo Aldeghi, Zeqing Bao, Florian Häse, Alán Aspuru‐Guzik, Christine Allen	182
7	ChemOS: An orchestration software to democratize autonomous discovery 2020 ·PLoS ONE ·Loı̈c M. Roch, Florian Häse, Christoph Kreisbeck, Teresa Tamayo-Mendoza, Lars P. E. Yunker, Jason E. Hein, Alán Aspuru‐Guzik	95
8	Machine learning for analysing ab initio molecular dynamics simulations 2020 ·Journal of Physics Conference Series ·Florian Häse, Ignacio Fdez. Galván, Alán Aspuru‐Guzik, Roland Lindh, Morgane Vacher	4
9	Designing and understanding light-harvesting devices with machine learning 2020 ·Nature Communications ·Florian Häse, Loı̈c M. Roch, Pascal Friederich, Alán Aspuru‐Guzik	85
10	From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning 2020 ·ACS Nano ·Loı̈c M. Roch, Semion K. Saikin, Florian Häse, Pascal Friederich, Randall H. Goldsmith, Salvador León, Alán Aspuru‐Guzik	13
11	Film Fabrication Techniques: Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems (Adv. Mater. 14/2020) 2020 ·Advanced Materials ·Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loı̈c M. Roch, Thomas Heumueller, Alán Aspuru‐Guzik, Christoph J. Brabec	1
12	SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry. 2019 ·arXiv (Cornell University) ·Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru‐Guzik	27
13	Oscillatory Active-Site Motions Correlate with Kinetic Isotope Effects in Formate Dehydrogenase 2019 ·ACS Catalysis ·(unknown), (unknown), (unknown), (unknown), (unknown), Florian Häse, (unknown), (unknown), Alán Aspuru‐Guzik, Dan Thomas Major, Lokesh Gakhar, Amnon Kohen, Christopher M. Cheatum	31
14	Next-Generation Experimentation with Self-Driving Laboratories 2019 ·Trends in Chemistry ·Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik	228
15	Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories 2018 ·Chemical Science ·Florian Häse, Loı̈c M. Roch, Alán Aspuru‐Guzik	107
16	Phoenics: A Bayesian Optimizer for Chemistry 2018 ·ACS Central Science ·Florian Häse, Loı̈c M. Roch, Christoph Kreisbeck, Alán Aspuru‐Guzik	260
17	Machine learning for quantum dynamics: deep learning of excitation energy transfer properties 2017 ·Chemical Science ·Florian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik	67
18	Absence of Selection for Quantum Coherence in the Fenna–Matthews–Olson Complex: A Combined Evolutionary and Excitonic Study 2017 ·ACS Central Science ·Stéphanie Valleau, Romain A. Studer, Florian Häse, Christoph Kreisbeck, Rafael G. Saer, Robert E. Blankenship, Eugene I. Shakhnovich, Alán Aspuru‐Guzik	13
19	Free energy analysis and mechanism of base pair stacking in nicked DNA 2016 ·Nucleic Acids Research ·Florian Häse, Martin Zacharias	43
20	A Compact Native 24-Residue Supersecondary Structure Derived from the Villin Headpiece Subdomain 2015 ·Biophysical Journal ·Henry G. Hocking, Florian Häse, Tobias Madl, Martin Zacharias, Matthias Rief, Gabriel Žoldák	6

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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