Valerio Rizzi

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Rizzi

First Name

Valerio

ORCID

0000-0001-5126-8996

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Publications 1 - 5 of 5

The role of water in host-guest interaction
Rizzi, Valerio; Bonati, Luigi; Ansari, Narjes; et al. (2021)
Nature Communications
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system’s degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail. © 2021, The Author(s).
Blind Search for Complex Chemical Pathways Using Harmonic Linear Discriminant Analysis
Rizzi, Valerio; Mendels, Dan; Sicilia, Emilia; et al. (2019)
Journal of Chemical Theory and Computation
Disentangling the mechanistic details of a chemical reaction pathway is a hard problem that often requires a considerable amount of chemical intuition and a component of luck. Experiments struggle in observing short-life metastable intermediates, while computer simulations often rely upon a good initial guess. In this work, we propose a method that, from the simulations of a reactant and a product state, searches for reaction mechanisms connecting the two by exploring the configuration space through metadynamics, a well-known enhanced molecular dynamics method. The key quantity underlying this search is based on the use of an approach called harmonic linear discriminant analysis which allows a systematic construction of collective variables. Given the reactant and product states, we choose a set of descriptors capable of discriminating between the two states. In order to not prejudge the results, generic descriptors are introduced. The fluctuations of the descriptors in the two states are used to construct collective variables. We use metadynamics in an exploratory mode to discover the intermediates and the transition states that lead from reactant to product. The search is at first conducted at a low theory level. The calculation is then refined, and the energy of the intermediates and transition states discovered during metadynamics is computed again using a higher level of theory. The method’s aim is to offer a simple reaction mechanism search procedure that helps in saving time and is able to find unexpected mechanisms that defy well established chemical paradigms. We apply it to two reactions, showing that a high level of complexity can be hidden even in seemingly trivial and small systems. The method can be applied to larger systems, such as reactions in solution or catalysis.
Data-Driven Collective Variables for Enhanced Sampling
Bonati, Luigi; Rizzi, Valerio; Parrinello, Michele (2020)
The Journal of Physical Chemistry Letters
The Onset of Dehydrogenation in Solid Ammonia Borane: An Ab Initio Metadynamics Study
Rizzi, Valerio; Polino, Daniela; Sicilia, Emilia; et al. (2019)
Angewandte Chemie. International Edition
Path integral molecular dynamics for bosons
Hirshberg, Barak; Rizzi, Valerio; Parrinello, Michele (2019)
Proceedings of the National Academy of Sciences of the United States of America

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Valerio Rizzi

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