Essential Elements

Department of Chemistry and Biochemistry
Organic/Inorganic/Materials Seminar Series

Professor Holger Bettinger, University of Tübingen
Monday – February 10, 2025
4:00 pm, 110 WIL
Hosted by Mike Haley

A Journey from Long Acenes to Cyclacenes

Acenes are a fundamentally and technologically important class of polycyclic aromatic hydrocarbons. Their small HOMO-LUMO gap is a blessing for materials properties but a curse for synthesis, characterization, and handling. My research group has achieved acenes of unprecedented lengths under the stabilizing conditions of matrix isolation and on-surface synthesis that allowed gaining an understanding of acene properties up to pentadecacene (15acene).[1] The key to success is the application of a protection group strategy that enables the release of acenes under these extreme conditions. The cyclic versions of acenes, cyclacenes, are unknown despite significant synthetic efforts since Edgar Heilbronner’s 1954 proposal. I will address expected properties of these zig-zag nanohoops and discuss strategies of their experimental realization using the low-temperature high-vacuum techniques in our laboratory.[2]

1. a) C. Tönshoff, H. F. Bettinger, Photogeneration of Octacene and Nonacene, Angew. Chem., Int. Ed. 2010, 49, 4125, 10.1002/anie.200906355; b) B. Shen, J. Tatchen, E. Sanchez-Garcia, H. F. Bettinger, Evolution of the Optical Gap in the Acene Series: Undecacene, Angew. Chem. Int. Ed. 2018, 57, 10506, 10.1002/anie.201802197; c) Z. Ruan, J. Schramm, J. B. Bauer, T. Naumann, H. F. Bettinger, R. Tonner-Zech, J. M. Gottfried, Synthesis of Tridecacene by Multistep Single-Molecule Manipulation, J. Am. Chem. Soc. 2024, 146, 3700, 10.1021/jacs.3c09392; d) Z. Ruan, J. Schramm, J. B. Bauer, T. Naumann, L. V. Müller, F. Sättele, H. F. Bettinger, R. Tonner-Zech, J. M. Gottfried, On-surface Synthesis and Characterization of Pentadecacene and its Gold Complexes, submitted for publication 2024.
2. a) D. Gupta, A. Omont, H. F. Bettinger, Energetics of Formation of Cyclacenes from 2,3-Didehydroacenes and Implications for Astrochemistry, Chem. Eur. J. 2021, 27, 4605, https://doi.org/10.1002/chem.202003045; b) J. B. Bauer, F. Diab, C. Maichle-Moessmer, H. Schubert, H. F. Bettinger, Synthesis of the [11]cyclacene framework by repetitive Diels-Alder cycloadditions, Molecules 2021, 26, 3047, 10.3390/molecules26103047; c) A. Somani, D. Gupta, H. F. Bettinger, Computational Studies of Dimerization of [n]-Cyclacenes, J. Phys. Chem. A 2024, 128, 6847, 10.1021/acs.jpca.4c02833.

Department of Chemistry and Biochemistry
Organic/Inorganic/Materials Seminar Series

Professor Professor Renana Gershoni Poranne, Technion
February 7, 2025
3:00 pm, WIL 110
Hosted by Mike Haley

Data are a Girl’s Best Friend: From High-Throughput Computation to Generative Deep Learning

Chemical databases are an essential tool for data-driven investigation of structure-property relationships and design of novel functional compounds, and they are the crucial foundation for machine- and deep-learning techniques, which efficiently map the chemical space and allow discovery of new molecular motifs of molecules and materials for various uses. However, there is a lack of suitable databases of polycyclic aromatic systems (PASs).

To enable the application of such techniques to the design of novel functional PASs, we established the COMPAS Project — a COMputational database of Polycyclic Aromatic Systems. This new database already contains over 500k molecules in three datasets: cata-condensed polybenzenoid hydrocarbons (COMPAS-1),¹ cata-condensed hetero-PASs (COMPAS-2),² and peri-condensed polybenzenoid hydrocarbons (COMPAS-3).³

With this new data in hand, we demonstrate the first examples of interpretable learning models in the chemical space of PASs. To this end, we developed two types of molecular representation to enable efficient and effective machine- and deep-learning models to train on the new data: a) a text-based representation⁴ and b) a graph-based representation.⁵ Our dedicated representations not only achieve higher predictive ability with fewer data, but are also amenable to interpretation – thus allowing the extraction of chemical insight from the model.

Using the COMPAS database and our dedicated representations, we implemented the first guided diffused-based model for inverse design of PASs: GaUDI.⁶ Our model generates new PASs with defined target properties. In addition to its flexible target function and high validity scores, GaUDI also accomplishes design of molecules with properties beyond the distribution of the training data.

References

(1)  Wahab, A.; Pfuderer, L.; Paenurk, E.; Gershoni-Poranne, R. The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: Cata-Condensed Polybenzenoid Hydrocarbons. J. Chem. Inf. Model. 2022, 62 (16), 3704.

(2)  Mayo Yanes, E.; Chakraborty, S.; Gershoni-Poranne, R. COMPAS-2: A Dataset of Cata-Condensed Hetero-Polycyclic Aromatic Systems. Sci. Data 2024, 11 (1), 97.

(3)  Wahab, A.; Gershoni-Poranne, R. COMPAS-3: A Data Set of Peri-Condensed Polybenzenoid Hydrocarbons. ChemRxiv February 26, 2024.

(4)  Fite, S.; Wahab, A.; Paenurk, E.; Gross, Z.; Gershoni-Poranne, R. Text-Based Representations with Interpretable Machine Learning Reveal Structure-Property Relationships of Polybenzenoid Hydrocarbons. J. Phys. Org. Chem. 2022, e4458.

(5)  Weiss, T.; Wahab, A.; Bronstein, A. M.; Gershoni-Poranne, R. Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons. J. Org. Chem. 2023, 88 (14), 9645–9656. https://doi.org/10.1021/acs.joc.2c02381.

(6)  Weiss, T.; Mayo Yanes, E.; Chakraborty, S.; Cosmo, L.; Bronstein, A. M.; Gershoni-Poranne, R. Guided Diffusion for Inverse Molecular Design. Nat. Comput. Sci. 2023, 3 (10), 873–882. https://doi.org/10.1038/s43588-023-00532-0.

Department of Chemistry and Biochemistry
Organic/Inorganic/Materials Seminar Series

Professor Jonathan Kuo, Penn State
January 31, 2025
3:00 pm, WIL 110
Hosted by ADSE

Comparing Metalloenzymatic Active Sites to Synthetic Model Complexes: Expanding Views on Supporting Ligands

Organic ligands alter the electronic structure and properties of the transition metals that they bind to. But what other functions can be programmed into metal/ligand complexes? In this talk, we will discuss how dynamic ligand features “unlock” key steps in (a) aerobic oxygenation and (b) electrophilic olefin activation. These dynamic features replicate dynamics present in enzymatic active sites. Recently, comparisons to enzymatic active sites have drawn us to host-guest-type ligand-metalate complexes – where the supporting ligand is designed to bind polyatomic metal anions [MO4]2– or [MCl4]2– via hydrogen bonding.

Department of Chemistry and Biochemistry
Physical Chemistry Seminar Series

Professor Shaowei Li, University of California San Diego
January 27, 2025 — 2:00pm, Tykeson 140
Hosted by: George Nazin

Title: On-Demand Control of Single-Molecule Chemistry through Vibrational Characterization and Manipulation

Chemical processes hinge on the dynamic rearrangement of nuclei within molecules. Controlling these motions with precision has been a long-standing goal in chemistry. My lab focuses on achieving this by manipulating interactions between individual molecules and their nanoscale environment. We engineer the potential energy surface at this scale to control molecular properties such as structure and reactivity. Using vibrational characterization techniques with scanning tunneling microscopy (STM)—including inelastic electron tunneling spectroscopy, action spectroscopy, and our recent advancement in single-molecule infrared absorption spectroscopy—we analyze molecular responses to the variation in the nano-chemical environment by examining their fingerprinting vibrational modes at the sub-molecular scale. By precisely tuning the junction geometry, material, and the interaction between the molecule and other surface entities, we can selectively influence specific bonds, paving the way for bond-selective control in chemistry.