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Found 11 papers, 5 papers with code
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
Deeptime: a Python library for machine learning dynamical models from time series data
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
Machine Learning Implicit Solvation for Molecular Dynamics
Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
Kernel methods for detecting coherent structures in dynamical data
In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score.
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.
Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity
sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters.
PotentialNet for Molecular Property Prediction
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.
Unsupervised learning of dynamical and molecular similarity using variance minimization
Then, we extend the method to partition two chemoinformatic datasets according to structural similarity to motivate a train/validation/test split for supervised learning that avoids overfitting.
Variational Encoding of Complex Dynamics
Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.
