Abstract
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.
| Original language | English |
|---|---|
| Journal | Computer Physics Communications |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Keywords
- Ab initio molecular dynamics
- Coupled cluster calculations
- Gradient domain machine learning
- Machine learning force field
- Machine learning potential
- Molecular property prediction
- Path integral molecular dynamics
- Quantum chemistry
ASJC Scopus subject areas
- Hardware and Architecture
- Physics and Astronomy(all)
Cite this
sGDML : Constructing accurate and data efficient molecular force fields using machine learning. / Chmiela, Stefan; Sauceda, Huziel E.; Poltavsky, Igor; Muller, Klaus; Tkatchenko, Alexandre.
In: Computer Physics Communications, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - sGDML
T2 - Constructing accurate and data efficient molecular force fields using machine learning
AU - Chmiela, Stefan
AU - Sauceda, Huziel E.
AU - Poltavsky, Igor
AU - Muller, Klaus
AU - Tkatchenko, Alexandre
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Ab initio molecular dynamics
KW - Coupled cluster calculations
KW - Gradient domain machine learning
KW - Machine learning force field
KW - Machine learning potential
KW - Molecular property prediction
KW - Path integral molecular dynamics
KW - Quantum chemistry
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UR - http://www.scopus.com/inward/citedby.url?scp=85062624346&partnerID=8YFLogxK
U2 - 10.1016/j.cpc.2019.02.007
DO - 10.1016/j.cpc.2019.02.007
M3 - Article
JO - Computer Physics Communications
JF - Computer Physics Communications
SN - 0010-4655
ER -