1 code implementation • Computer Physics Communications 2019 • Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A.
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
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.
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.