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A unified framework for machine learning collective variables for enhanced sampling simulations:
Luigi Bonati, Enrico Trizio, Andrea Rizzi +1
Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several…
Multimap targeted free energy estimation
Andrea Rizzi, Paolo Carloni, Michele Parrinello
We present a new method to compute free energies at a quantum mechanical (QM) level of theory from molecular simulations using cheap reference potential energy functions, such as f…
Characterizing metastable states with the help of machine learning
Pietro Novelli, Luigi Bonati, Massimiliano Pontil +1
Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature is becom…
Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling
Michele Invernizzi, Michele Parrinello
In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collec…
Deep learning the slow modes for rare events sampling
Luigi Bonati, GiovanniMaria Piccini, Michele Parrinello
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resou…
From enhanced sampling to reaction profiles
Enrico Trizio, Michele Parrinello
The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect…