Search Results for author: Sauceda

Found 3 papers, 3 papers with code

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

1 code implementation Nature Communications 2018 Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A.

We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules.

Machine Learning of Accurate Energy-conserving Molecular Force Fields

1 code implementation Science Advances 2017 Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R.

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.

BIG-bench Machine Learning

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