A high-bias, low-variance review of data-driven collective variable discovery and enhanced sampling

Andrew Ferguson
April 22, 2024

Towards quantitative prediction of kinetic rates and optimal reaction coordinates from the projected dynamics of transition paths

Karen Palacio-Rodriguez
April 22, 2024

Dimension reduction from the user’s perspective

Marina Meila
April 22, 2024

Learning the committor, free energy profile, and rates with ML-driven path sampling simulations

Roberto Covino
April 22, 2024

Graph atomic cluster expansion and message passing interatomic potentials

Anton Bochkarev
April 12, 2024

Sampling Complex Energy Landscapes in Material Science Using Data-Driven Force Fields

Cosmin Marinica
April 12, 2024

AIMNet2 family of machine learning potentials: general-purpose and task-specific models for element-organic molecules and radicals, reactions and molecular crystals

Roman Zubatyuk
April 12, 2024

Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies

Elena Gelzinyte
April 11, 2024

Charge and other advances in the Ch.ACE for next generation interatomic potentials

James Goff
April 11, 2024