159 citations · 457 across the 18 of their papers we have counts for
18 papers
Atomistic Modeling for Electro-chemical Reactions
K. Doblhoff-Dier, B. Ballotta, L. Bonati +16
Computational modeling plays a central role in advancing our understanding of electro-chemical reactions and may thus guide the rational development of sustainable energy technolog…
SelfTICA: contrastive learning of dynamical representations for rare-event sampling and characterization
Kai Zhu, Jintu Zhang, Pietro Novelli +2
Rare events govern many important molecular processes but remain difficult to characterize within accessible simulation timescales. This challenge has motivated machine-learning me…
Electrified Metal-Water Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
Michele Giovanni Bianchi, Michele Re Fiorentin, Francesca Risplendi +4
Simulating electrified metal-water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively exp…
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
Kai Zhu, Enrico Trizio, Jintu Zhang +4
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constraine…
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Giacomo Turri, Luigi Bonati, Kai Zhu +2
We introduce an encoder-only approach to learn the evolution operators of large-scale non-linear dynamical systems, such as those describing complex natural phenomena. Evolution op…
Weighted Active Space Protocol for Multireference Machine-Learned Potentials
Aniruddha Seal, Simone Perego, Matthew R. Hennefarth +5
Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant…
Fast and Fourier Features for Transfer Learning of Interatomic Potentials
Pietro Novelli, Giacomo Meanti, Pedro J. Buigues +4
Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To thi…
PLUMED Tutorials: a collaborative, community-driven learning ecosystem
Gareth A. Tribello, Massimiliano Bonomi, Giovanni Bussi +60
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific p…
Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
Enrico Trizio, Andrea Rizzi, Pablo M. Piaggi +2
Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external po…
Descriptors-free Collective Variables From Geometric Graph Neural Networks
Jintu Zhang, Luigi Bonati, Enrico Trizio +4
Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables…
Combining transition path sampling with data-driven collective variables through a reactivity-biased shooting algorithm
Jintu Zhang, Odin Zhang, Luigi Bonati +1
Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representation…
Deep learning path-like collective variable for enhanced sampling molecular dynamics
Thorben Fröhlking, Luigi Bonati, Valerio Rizzi +1
Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. Existing variables that describe the progression…
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…
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
John Falk, Luigi Bonati, Pietro Novelli +2
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, whi…
Unraveling the Crystallization Kinetics of the GeSbTe Phase Change Compound with a Machine-Learned Interatomic Potential
Omar Abou El Kheir, Luigi Bonati, Michele Parrinello +1
The phase change compound GeSbTe (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible…
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…
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…
Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
Luigi Bonati, Michele Parrinello
Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both semiconducting solid and meta…