Michele Parrinello

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Prof. in Computational Sciences

Italian Institute of Technology

Italy

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Prof. Michele Parrinello is a faculty member in Computational Sciences at the Italian Institute of Technology in Italy. His research focuses on the development of machine learning techniques for molecular dynamics and enzyme catalysis, as evidenced by his recent articles on topics such as multireference potentials and ammonia decomposition. He is dedicated to advancing the understanding of complex dynamical systems in chemical processes.

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VARMET

Open Date: 2015-01-01

Close Date: 2020-12-01

Michele Parrinello

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Articles (29)

Weighted active space protocol for multireference machine-learned potentials

No Time for Nitrides: How Cobalt Alloying Promotes Iron Catalysts for Ammonia Decomposition

Coordinated Residue Motions at the Enzyme–Substrate Interface Promote DNA Translocation in Polymerases

A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis

Everything everywhere all at once: a probability-based enhanced sampling approach to rare events

Slow dynamical modes from static averages

Machine learning-driven molecular dynamics unveils a bulk phase transformation driving ammonia synthesis on barium hydride

Iron Nitride Formation and Decomposition during Ammonia Decomposition over a Wustite-Based Bulk Iron Catalyst

How Dynamics Changes Ammonia Cracking on Iron Surfaces

A Stochastic Landscape Approach for Protein Folding State Classification

Correction to A Stochastic Landscape Approach for Protein Folding State Classification

Computing the committor with the committor to study the transition state ensemble

Intramolecular and Water Mediated Tautomerism of Solvated Glycine

Effective data-driven collective variables for free energy calculations from metadynamics of paths

Data-driven classification of ligand unbinding pathways

Structure and polymerization of liquid sulfur across the λ-transition

Descriptor-Free Collective Variables from Geometric Graph Neural Networks

Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables

Reactant-induced dynamics of lithium imide surfaces during the ammonia decomposition process

A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar

Deep learning collective variables from transition path ensemble

Free energies at QM accuracy from force fields via multimap targeted estimation

Water regulates the residence time of Benzamidine in Trypsin

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

Breviarium de Motu Simulato Ad Atomos Pertinenti

Prediction of a Supersolid Phase in High-Pressure Deuterium

Confinement effects and acid strength in zeolites

A modified nudged elastic band algorithm with adaptive spring lengths

Liquid-Liquid Critical Point in Phosphorus

Collaborators (3)

GiovanniMaria Piccini

Barak Hirshberg

Dhiman Ray

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