INVESTIGADORES
MILLÁN RaÚl Daniel
congresos y reuniones científicas
Título:
Machine Learning in Multiscale Modelling of Molecular Systems
Autor/es:
BEHROOZ HASHEMIAN; DANIEL MILLÁN; MARINO ARROYO
Lugar:
Manchester
Reunión:
Conferencia; Multiscale Modelling of Condensed Phase and Biological Systems; 2014
Institución organizadora:
Collaborative Computational Project for Biomolecular Simulation
Resumen:
Multiscale modelling of complex molecular systems, such as proteins,
requires to bridge the gap between femtosecond time scale of molecular
vibrations and millisecond-and-up time scale of thermodynamic
observables. Due to this huge gap, molecular dynamics (MD) simulations
experience insufficient sampling, which hamper their connection with experiments. Enhanced sampling methods are thus in great demand to
overcome this challenge; however, the effectiveness of such methods
relies on having a good set of collective variables (CVs), which govern
the essential dynamics of the system. Identifying such a coarse model is
far from obvious for complex systems. We present here a general method,
smooth and nonlinear data-driven collective variables (SandCV),
based on machine learning techniques to identify such CVs from available
computational or experimental ensembles, and integrate them in enhanced
sampling methods. SandCV is a versatile method and can be
non-intrusively combined with the available molecular dynamics
implementations.