In a study recently published in Communications Chemistry, researchers from ICFO and partners of OPTOlogic report on a newly-developed machine learning algorithm for LIED to extract the three-dimensional structure of large and complex molecules.
Revolutionary Discovery: Visualizing Molecular Transformation
Until very recently, the very idea of witnessing how molecules break or transform during chemical reactions was unfathomable. However, in 2016, researchers from ICFO achieved a breakthrough by developing mid-IR-driven laser-induced electron diffraction (LIED) with kinematic coincidence detection. This groundbreaking technique enabled them to image the position of each atom within a single molecule using one of its own electrons. The remarkable picometer spatial and attosecond temporal resolution achieved through LIED allowed them to actually image and track the molecular bond breakup in acetylene (C2H2) a mere nine femtoseconds after its ionization, a method they aptly coined as the “molecular selfie”.
Application of LIED to Small Molecules: A Glimpse into the Molecular World
Initially, applying the LIED technique to capture snapshots of small gas-phase molecules proved to be an immensely powerful tool for understanding the intricate interactions of molecules, revealing how they react, change, break, and bend. Nevertheless, extending this technique to more complex molecular structures posed a significant challenge. As the size of the molecule increases, so does the difficulty of structural retrieval. Consequently, it becomes necessary to calculate thousands of molecular configurations for all possible orientations, a task that could take an impractical amount of time.
In a recent study published in Chemistry Communications, ICFO researcher Xinyao Liu, Kasra Amini, Aurelien Sanchez, Blanca Belsa, Tobias Steinle, led by led by ICREA Professor at ICFO Jens Biegert, report on a solution to this problem with a newly-developed machine learning algorithm for LIED to extract the three-dimensional structure of large and complex molecules in their experiment, the team of researchers developed a machine learning model and combined it with a Convolutional Neural Network (CNN) algorithm which, according to the researchers, is “well suited for problems in image recognitions to identified subtle features from an image at a different level of complexity similar to a human brain”. Using the CNN-ML framework, the pre-calculated database of configurations could be drastically reduced to unambiguously identify a complex chiral molecular structure such as the Fenchone molecule.
Importancia del Avance: Determinación Eficiente de Estructuras Moleculares 3D
This result is of major importance because being able to calculate the 3D molecular structure of complex molecules with the sufficient structural resolution has been, so far, a very difficult challenge to overcome. This study is a major step forward in this field, where the combination of LIED, machine learning and CNN network, have not only shown that they can predict and determine the structure of these large molecules but also do it within a completely reasonable computing processing time.
Cited article: A. Sanchez, K. Amini, S.-J. Wang, T. Steinle, B. Belsa, J. Danek, A. T. Le, X. Liu, R. Moshammer, T. Pfeifer, M. Richter, J. Ullrich, S. Gräfe, C. D. Lin & J. Biegert. Molecular structure retrieval directly from laboratory-frame photoelectron spectra in laser-induced electron diffraction Communications Chemistry volume 12, Article number: 1520 (2021).
