Thesis Proposals
Computational Modeling of Organic Molecules Adsorbed at Metal Surfaces (Supervisors: Sergio Rampino, Antonino Polimeno)
The physics and chemistry of organic molecules adsorbed at metal surfaces plays an important role in many applications of scientific and technological interest including catalysis, light-emitting diodes, single-molecule junctions, molecular sensors and switches, and photovoltaics [1]. This thesis project will focus on the computational modeling of the structure and dynamics of such hybrid organic/inorganic systems via Density Functional Theory (DFT), with emphasis on the interpretation of experimental results including Scanning Tunneling Microscopy (STM) [2], capable of rendering images of the adsorbed molecules with atomic-level resolution.
[1] Liu W, Tkatchenko A, Scheffler M, Modeling Adsorption and Reactions of Organic Molecules at Metal Surfaces, Accounts Chemical Research 47, 3369–3377 (2014)
[2] Zandvliet HJ, van Houselt A, Scanning Tunneling Spectroscopy, Annual Review of Analytical Chemistry 2, 37–55 (2009)
Machine-Learning Framework for the Characterization of Chemical Bonding (Supervisor: Sergio Rampino)
Chemistry is discussed and rationalized in terms of concepts rooted in quantum mechanics that have their physical counterpart in large, intricate and often opaque data produced by solving the Schrödinger equation. This thesis project aims at developing a machine-learning framework for the characterization of chemical bonding based on volumetric data of molecular electron densities obtained by state-of-the-art quantum-chemistry calculations, with a focus on latent relationships between hidden data features and well-established concepts (e.g., donation, backdonation, hydrogen bonding). Applications will include comparison with available bond-analysis methods [1, 2] and the characterization of molecular systems exhibiting controversial features.
[1] Bader RFW, A Quantum Theory of Molecular Structure and its Applications, Chemical Reviews 91, 893–928 (1991)
[2] Nottoli G, Ballotta B, Rampino S, Local Charge-Displacement Analysis: Targeting Local Charge-Flows in Complex Intermolecular Interactions, The Journal of Chemical Physics 157, 084107, 13pp (2022)
Generative Artificial-Intelligence Models for Molecular and Material Discovery (Supervisors: Sergio Rampino, Antonino Polimeno)
Generative artificial-intelligence (AI) models have recently emerged as a new paradigm for molecular discovery [1, 2]. Within these models, a discrete molecular space is typically mapped into a continuous latent space which can then be explored for molecule extrapolation or interpolation driven by some optimization criteria. This thesis project will focus on the development and application of generative AI models for the discovery of new molecules featuring specific desired properties, with applications including drug discovery (e.g., targeting high efficiency for specific therapeutic purposes), advanced materials for energy harvesting, storage and conversion, and the design of sustainable deep-eutectic solvents for raw-material recycle.
[1] Anstine DM, Isayev O, Generative Models as an Emerging Paradigm in the Chemical Sciences, Journal of the American Chemical Society 145, 8736–8750 (2023)
[2] Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen F, Generative Models for Molecular Discovery: Recent Advances and Challenges, WIREs Comput Mol Sci 12, e1608 (2022)