Developing accurate and efficient coarse-grained representations of prot...
The development of efficient machine learning models for molecular syste...
Intrinsically disordered proteins participate in many biological process...
A generalized understanding of protein dynamics is an unsolved scientifi...
Machine learning potentials are an important tool for molecular simulati...
The prediction of quantum mechanical properties is historically plagued ...
Parametric and non-parametric machine learning potentials have emerged
r...
Molecular dynamics simulations provide a mechanistic description of mole...
Coarse graining enables the investigation of molecular dynamics for larg...
Solving sparse reward tasks through exploration is one of the major
chal...
Reinforcement learning (RL) has been very successful in recent years but...
Many aspects of the study of protein folding and dynamics have been affe...
Molecular simulations produce very high-dimensional data-sets with milli...