Pascal Friederich

6.7k total citations · 5 hit papers
125 papers, 4.0k citations indexed

About

Pascal Friederich is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Pascal Friederich has authored 125 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Electrical and Electronic Engineering, 52 papers in Materials Chemistry and 26 papers in Molecular Biology. Recurrent topics in Pascal Friederich's work include Machine Learning in Materials Science (38 papers), Organic Electronics and Photovoltaics (34 papers) and Organic Light-Emitting Diodes Research (25 papers). Pascal Friederich is often cited by papers focused on Machine Learning in Materials Science (38 papers), Organic Electronics and Photovoltaics (34 papers) and Organic Light-Emitting Diodes Research (25 papers). Pascal Friederich collaborates with scholars based in Germany, Canada and United States. Pascal Friederich's co-authors include Alán Aspuru‐Guzik, Florian Häse, Wolfgang Wenzel, AkshatKumar Nigam, Mario Krenn, Jonny Proppe, Gabriel dos Passos Gomes, Franz Symalla, Patrick Reiser and Robert Pollice and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Advanced Materials.

In The Last Decade

Pascal Friederich

118 papers receiving 4.0k citations

Hit Papers

align trajectories sort by overperformance

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 Florian Häse, Pascal Friederich et al. profile →
Graph neural networks for materials science and chemistry

2022 407 citations Patrick Reiser, Marlen Neubert et al. profile →
Machine-learned potentials for next-generation matter simulations

2021 376 citations Pascal Friederich, Florian Häse et al. Nature Materials profile →
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

2022 192 citations Gabriel dos Passos Gomes, Pascal Friederich et al. profile →
On scientific understanding with artificial intelligence

2022 186 citations Robert Pollice, Pascal Friederich et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Pascal Friederich	2.0k	1.4k	803	633	407	125	4.0k
Rohit Batra	3.2k 1.6×	1.3k 0.9×	793 1.0×	468 0.7×	216 0.5×	54	4.6k
Wencong Lu	2.8k 1.3×	1.4k 1.0×	607 0.8×	1.2k 1.9×	305 0.7×	199	6.2k
Rafael Gómez‐Bombarelli	2.4k 1.2×	1.4k 1.0×	1.3k 1.7×	1.1k 1.7×	184 0.5×	126	5.1k
Daniel W. Davies	2.6k 1.3×	810 0.6×	693 0.9×	332 0.5×	112 0.3×	49	3.9k
Woo Youn Kim	2.4k 1.2×	1.7k 1.3×	672 0.8×	760 1.2×	373 0.9×	83	4.9k
Hugh Cartwright	2.2k 1.1×	680 0.5×	692 0.9×	599 0.9×	111 0.3×	48	3.9k
Amir Barati Farimani	2.7k 1.3×	1.1k 0.8×	619 0.8×	848 1.3×	112 0.3×	142	5.5k
Kun Yao	1.4k 0.7×	391 0.3×	506 0.6×	491 0.8×	327 0.8×	68	2.8k
Yu‐Ting Lin	1.1k 0.6×	1.4k 1.0×	1.1k 1.4×	1.1k 1.7×	498 1.2×	105	4.0k
Yanyan Li	4.2k 2.1×	4.3k 3.1×	1.0k 1.3×	398 0.6×	511 1.3×	387	10.7k

Countries citing papers authored by Pascal Friederich

Since Specialization

Citations

This map shows the geographic impact of Pascal Friederich'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 Pascal Friederich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Friederich more than expected).

Fields of papers citing papers by Pascal Friederich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pascal Friederich. 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 Pascal Friederich. The network helps show where Pascal Friederich may publish in the future.

Co-authorship network of co-authors of Pascal Friederich

This figure shows the co-authorship network connecting the top 25 collaborators of Pascal Friederich. A scholar is included among the top collaborators of Pascal Friederich 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 Pascal Friederich. Pascal Friederich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Huang, Yu‐Chieh, et al.. (2025). Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data. Journal of Cheminformatics. 17(1). 24–24. 2 indexed citations

Qādir, Ghulām, et al.. (2025). Predicting hydrogen atom transfer energy barriers using Gaussian process regression. Digital Discovery. 4(2). 513–522. 2 indexed citations

Zhou, Chen, Marlen Neubert, Y. Koide, et al.. (2025). PAL – parallel active learning for machine-learned potentials. Digital Discovery. 4(7). 1901–1911. 2 indexed citations

Reiser, Patrick, Christian Kupfer, Anastasia Barabash, et al.. (2025). Tuning the Transparency and Exciton Transition of D‐π‐A‐π‐D Type Small Molecules. Chemistry - A European Journal. 31(45). e00657–e00657.

Friederich, Pascal, et al.. (2024). Development of aluminum oxide slurries for additive manufacturing by Bayesian optimization. Open Ceramics. 20. 100705–100705.

Reiser, Patrick, et al.. (2024). Connectivity optimized nested line graph networks for crystal structures. Digital Discovery. 3(3). 594–601. 22 indexed citations

Reiser, Patrick, et al.. (2024). Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins. Chemical Science. 15(7). 2518–2527. 7 indexed citations

Schweidler, Simon, Henrik Schopmans, Patrick Reiser, et al.. (2023). Synthesis and Characterization of High‐Entropy CrMoNbTaVW Thin Films Using High‐Throughput Methods. Advanced Engineering Materials. 25(2). 2 indexed citations

Fediai, Artem, et al.. (2023). Interpretable delta-learning of GW quasiparticle energies from GGA-DFT. Machine Learning Science and Technology. 4(3). 35045–35045. 4 indexed citations

10.

Schopmans, Henrik, Patrick Reiser, & Pascal Friederich. (2023). Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms. Digital Discovery. 2(5). 1414–1424. 4 indexed citations

11.

Kulagin, Roman, Patrick Reiser, Arnd Koeppe, et al.. (2023). Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimization. Advanced Engineering Materials. 25(13). 8 indexed citations

12.

Reiser, Patrick, et al.. (2023). Modeling Charge Transport in Organic Semiconductors Using Neural Network Based Hamiltonians and Forces. Journal of Chemical Theory and Computation. 19(13). 3825–3838. 5 indexed citations

13.

Friederich, Pascal, et al.. (2023). Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations. SHILAP Revista de lepidopterología. 1(4).

14.

Pollice, Robert, Pascal Friederich, Cyrille Lavigne, Gabriel dos Passos Gomes, & Alán Aspuru‐Guzik. (2021). Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates. Matter. 4(5). 1654–1682. 126 indexed citations

15.

Behboodi‐Sadabad, Farid, Wenxi Lei, Timo Sommer, et al.. (2021). High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines. Materials Today Bio. 10. 100108–100108. 12 indexed citations

16.

Friederich, Pascal, Florian Häse, Jonny Proppe, & Alán Aspuru‐Guzik. (2021). Machine-learned potentials for next-generation matter simulations. Nature Materials. 20(6). 750–761. 376 indexed citations breakdown →

17.

Bag, Saientan, et al.. (2021). Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies. Journal of Chemical Theory and Computation. 17(11). 7195–7202. 4 indexed citations

18.

Friederich, Pascal, Salvador León, José Darío Perea, Loı̈c M. Roch, & Alán Aspuru‐Guzik. (2020). The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study. Machine Learning Science and Technology. 2(1). 01LT01–01LT01. 11 indexed citations

19.

Bag, Saientan, Pascal Friederich, Ivan Kondov, & Wolfgang Wenzel. (2019). Concentration dependent energy levels shifts in donor-acceptor mixtures due to intermolecular electrostatic interaction. Scientific Reports. 9(1). 12424–12424. 14 indexed citations

20.

Li, Jing, Ivan Duchemin, Otello Maria Roscioni, et al.. (2018). Host dependence of the electron affinity of molecular dopants. Materials Horizons. 6(1). 107–114. 65 indexed citations

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.

Explore authors with similar magnitude of impact