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66 Publications visible to you, out of a total of 66
Understanding and exploiting interfacial interactions between phosphonic acid functional groups and co-evaporated perovskites

Abstract (Expand)

Interfacial engineering has fueled recent development of p-i-n perovskite solar cells (PSCs), with self-assembled monolayer-based hole-transport layers (SAM-HTLs) enabling almost lossless contacts for … solution-processed PSCs, resulting in the highest achieved power conversion efficiency (PCE) to date. Substrate interfaces are particularly crucial for the growth and quality of co-evaporated PSCs. However, adoption of SAM-HTLs for co-evaporated perovskite absorbers is complicated by the underexplored interaction of such perovskites with phosphonic acid functional groups. In this work, we highlight how exposed phosphonic acid functional groups impact the initial phase and final bulk crystal structures of co-evaporated perovskites and their resultant PCE. The explored surface interaction is mediated by hydrogen bonding with interfacial iodine, leading to increased formamidinium iodide adsorption, persistent changes in perovskite structure, and stabilization of bulk α-FAPbI3, hypothesized as being due to kinetic trapping. Our results highlight the potential of exploiting substrates to increase control of co-evaporated perovskite growth.

Authors: Thomas Feeney, Julian Petry, Abderrezak Torche, Dirk Hauschild, Benjamin Hacene, Constantin Wansorra, Alexander Diercks, Michelle Ernst, Lothar Weinhardt, Clemens Heske, Ganna Gryn’ova, Ulrich W. Paetzold, Paul Fassl

Date Published: 1st Mar 2024

Publication Type: Journal

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery

Abstract (Expand)

Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA–presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite … to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an individual’s unmanipulated, complete human leukocyte antigen (HLA) haplotype. Combining a highly sensitive TCR signaling reporter with an antigen processing system to overcome previously undescribed limitations to target expression, T-FINDER both robustly identifies unknown peptide:HLA ligands from antigen libraries and rapidly screens and functionally validates the specificity of large TCR libraries against known or predicted targets. To demonstrate its capabilities, we apply the platform to multiple TCR-based applications, including diffuse midline glioma, celiac disease, and rheumatoid arthritis, providing unique biological insights and showcasing T-FINDER’s potency and versatility.

Authors: Miray Cetin, Veronica Pinamonti, Theresa Schmid, Tamara Boschert, Ana Mellado Fuentes, Kristina Kromer, Taga Lerner, Jing Zhang, Yonatan Herzig, Christopher Ehlert, Miguel Hernandez-Hernandez, Georgios Samaras, Claudia Maldonado Torres, Laura Fisch, Valeriia Dragan, Arlette Kouwenhoven, Bertrand Van Schoubroeck, Hans Wils, Carl Van Hove, Michael Platten, Edward W. Green, Frederik Stevenaert, Nathan J. Felix, John M. Lindner

Date Published: 2nd Feb 2024

Publication Type: Journal

Accelerated chemical science with AI

Abstract (Expand)

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and …educators, and academic bodies were provided.

Authors: Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz Grzybowski, Geun Ho Gu, Jason Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von Lilienfeld, Aron Walsh

Date Published: 17th Jan 2024

Publication Type: Journal

Host–guest interactions in framework materials: Insight from modeling

Abstract (Expand)

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily …xtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science.

Authors: Michelle Ernst, Jack D. Evans, Ganna Gryn'ova

Date Published: 1st Dec 2023

Publication Type: Journal

Crafting molecular architectures with guided diffusion

Abstract

Not specified

Author: Ganna Gryn’ova

Date Published: 11th Oct 2023

Publication Type: Journal

CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions

Abstract

Not specified

Authors: Christopher Ehlert, Anna Piras, Ganna Gryn’ova

Date Published: 3rd Oct 2023

Publication Type: Journal

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

Abstract (Expand)

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as … solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Authors: Rostislav Fedorov, Ganna Gryn’ova

Date Published: 8th Aug 2023

Publication Type: Journal

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