References of "Chmiela, Stefan"
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See detailsGDML: Constructing accurate and data efficient molecular force fields using machine learning
Chmiela, Stefan; Sauceda, Huziel; Poltavsky, Igor et al

in Computer Physics Communications (2019), 240

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces ... [more ▼]

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations. [less ▲]

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See detailMolecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Sauceda, Huziel; Chmiela, Stefan; Poltavsky, Igor et al

in Journal of Chemical Physics (2019), 150

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al ... [more ▼]

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018) and Chmiela et al., Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the “gold standard” coupled-cluster theory with single, double and perturbative triple excitations [CCSD(T)]. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g., H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion, and n → π* interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy. [less ▲]

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See detailTowards exact molecular dynamics simulations with machine-learned force fields
Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert et al

in Nature Communications (2018), 9

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See detailSchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, Kristof T.; Kindermans, P. J.; Sauceda, Huziel E. et al

in 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA (2017, December)

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See detailMachine learning of accurate energy-conserving molecular force fields
Chmiela, Stefan; Tkatchenko, Alexandre UL; Sauceda, Huziel et al

in Science Advances (2017), 3

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 ... [more ▼]

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. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. [less ▲]

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See detailQuantum-chemical insights from deep tensor neural networks
Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan et al

in Nature Communications (2017), 8

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