Molecular propensity as a driver for explorative reactivity studies

Quantum chemical studies of reactivity involve calculations on a large number of molecular structures and comparison of their energies. Already the set-up of these calculations limits the scope of the results that one will obtain, because several system-specific variables such as the charge and spin need to be set prior to the calculation. For a reliable exploration of reaction mechanisms, a considerable number of calculations with varying global parameters must be taken into account, or important facts about the reactivity of the system under consideration can go undetected. For example, one could miss crossings of potential energy surfaces for different spin states or might not note that a molecule is prone to oxidation. Here, we introduce the concept of molecular propensity to account for the predisposition of a molecular system to react across different electronic states in certain nuclear configurations. Within our real-time quantum chemistry framework, we developed an algorithm that allows us to be alerted to such a propensity of a system under consideration.Comment: 10 pages, 7 figure

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Long-time Low-latency Quantum Memory by Dynamical Decoupling

Quantum memory is a central component for quantum information processing devices, and will be required to provide high-fidelity storage of arbitrary states, long storage times and small access latencies. Despite growing interest in applying physical-layer error-suppression strategies to boost fidelities, it has not previously been possible to meet such competing demands with a single approach. Here we use an experimentally validated theoretical framework to identify periodic repetition of a high-order dynamical decoupling sequence as a systematic strategy to meet these challenges. We provide analytic bounds-validated by numerical calculations-on the characteristics of the relevant control sequences and show that a "stroboscopic saturation" of coherence, or coherence plateau, can be engineered, even in the presence of experimental imperfection. This permits high-fidelity storage for times that can be exceptionally long, meaning that our device-independent results should prove instrumental in producing practically useful quantum technologies.Comment: abstract and authors list fixe

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts

Computational development of models and tools for the kinetic study of astrochemical gas-phase reactions

This PhD thesis focuses on the application and development of computational tools and methodologies for the modeling of the kinetics of gas-phase reactions of astrophysical interest in the interstellar medium (ISM). The complexity related to the investigation of chemical reactivity in space is mostly due to the extreme physical conditions of temperature, pressure and exposure to high-energy radiation, which in turn also lead to the formation of exotic species, like radicals and ions. Nevertheless, there is still much to be understood about the formation of molecules, the major issue being the lack of sufficient laboratory (experimental and computational) studies. A more detailed and accurate study of all the chemical processes occurring in the ISM will allow us to obtain the data necessary to simulate the chemical evolution of an interstellar cloud over time using kinetic models including thousands of reactions that involve hundreds of species. The collection of the kinetic parameters required for the relevant reactions has led to the growth of different astrochemical databases, such as KIDA and UMIST. However, the data gathered in these catalogues are incomplete, and rely extensively on crude estimations and extrapolations. These rates are of paramount importance to get a better comprehension of the relative abundances of the chemical compounds extrapolated by the astronomers from the spectral data recorded through the radio telescopes and the in-orbit devices, like the satellites. Accurate state-of-the-art computational approaches play a fundamental role in analyzing feasible reaction mechanisms and in accurately predicting the associated kinetics. Such approaches usually rely on chemical intuition where a by-hand search of the most likely pathways is performed. Unfortunately, thisprocedure can lead to overlook significant mechanisms, especially when large molecular systems are investigated. Increasing the size of a molecule can also increase the number of its possible conformers which can show a different chemical reactivity with respect to the same chemical partner. This brings to get very complex chemical reaction networks in which hundreds of chemical species are involved and thousands of chemical reactions can occur.During the last decades, a lot of effort has been done to develop computational techniques able to perform extensive and thorough investigations of complex reaction mechanisms. Such approaches rely on automated computational protocols which drastically decrease the risk of making blunders during the search for significant reaction pathways.Furthermore, the accurate characterization of the potential energy surfaces (PESs) critical points, like reactants, intermediates, transition states and products involved in the reaction mechanism, is crucial in order to carry out a reliable kinetic investigation. The kinetic analysis of an erroneous potential energy surface, would lead to gross errors in the estimation of the rate constants of the chemical species involved in the reaction.In order to avoid such errors, the combination of high-level electronic structure calculations via composite scheme can be helpful to get a more precise estimation of the energy barriers involved in the reaction mechanism. It has been proven that "cheap"[1] composite schemes can achieve subchemical accuracy without any empirical parameters and with convenient computation times, making them perfect for the purpose of this thesis.In recent decades, many efforts have been made to develop theoretical and computational methodologies to perform accurate numerical simulations of the kinetics of such complex reaction mechanisms in a wide range of thermodynamic conditions that mimic extreme reaction environmentsas for combustion systems, the atmosphere and the ISM. Such methodologies are based on the ab initio-transition-state-theory-based master equation approach, which allows the determination of rate coefficients and branching ratios of chemical species involved in complex chemical reactions. This methodology allows to make accurate predictions of the relative abundances of the reaction products for complex reactions even under conditions of temperature and pressure not experimentally accessible, such as those that characterize the ISM. Based on these premises, this dissertation has been focused on the application of a computational protocol for the ab initio-based computational modeling and kinetic investigation of gas-phase reactions which can occur in the ISM.This protocol is based on the application of validated methodologies for the automated discovery of complex reaction mechanisms by means of the AutoMeKin[2] program, the accurate calculation of the energetic of the potential energy surfaces (PESs) through the junChS and junChS-F12a "cheap" composite schemes and the kinetic investigation using the StarRate computer program specifically designed to study gas-phase reactions of astrochemical interest in conjunction with the MESS program. Furthermore, this dissertation has been also focused on the development and implementation of StarRate, a computer program for the accurate calculation of kinetics through a chemical master equation approach of multi-step chemical reactions. StarRate is an object-based program written in the so-called F language. It is structured in three main modules, namely molecules, steps and reactions, which extract the properties needed to calculate the kinetics for the single-step reactions partecipating in the overall reaction. Another module, in_out, handles program’s input and output operations. The main program,starrate, controls the sequences of the calling of the procedures contained in each of the three main modules.Through these modular structure, StarRate[3] can compute canonical and microcanonical rate coefficients taking into account for the tunneling effect and the energy-dependent and time-dependent evolution of the species concentrations involved in the reaction mechanism. Such protocol has been applied to investigate the formation reaction mechanisms of some complex interstellar polyatomic molecules, named interstellar complex organic molecules (iCOMs). More specifically, the formation of prebiotic iCOMs in space has raised considerable interest in the scientific community, because they are considered as precursors of more complex biological systems involved in the origin of life in the Universe. Debate on the origins of these biomolecular building blocks has been further stimulated by the discovery of nucleobases and amino acids in meteorites and other extraterrestrial sources. However, few insights on the chemistry which brings to the formation of such compounds is known.  References: [1] Jacopo Lupi,Silvia Alessandrini,Cristina Puzzarini,and Vincenzo Barone.junchs and junchs-F12 models:Parameter-free efficient yet accurate compositeschemes for energies and structures of noncovalent complexes. Journal of Chem-ical Theory and Computation, 17(11):6974–6992, 2021. PMID: 34677974.[2] Emilio Martínez-Núñez, George L. Barnes, David R. Glowacki, Sabine Kopec,Daniel Peláez, Aurelio Rodríguez, Roberto Rodríguez-Fernández, Robin J. Shan-non, James J. P. Stewart, Pablo G. Tahoces, and Saulo A. Vazquez.Au-tomekin2021: An open-source program for automated reaction discovery. Journalof Computational Chemistry, 42(28):2036–2048, 2021.[3] Surajit Nandi, Bernardo Ballotta, Sergio Rampino, and Vincenzo Barone.Ageneral user-friendly tool for kinetic calculations of multi-step reactions withinthe virtual multifrequency spectrometer project. Applied Sciences, 10(5), 2020