10 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…
Contrastive learning of dynamical representations for enhanced molecular sampling
Kai Zhu, Jintu Zhang, Pietro Novelli +2
Identifying collective variables that capture slow dynamical modes is essential for sampling rare events in complex systems. Existing machine-learning approaches often require pred…
Electrochemical 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…