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 - Müller, Klaus Robert
AU - Tkatchenko, Alexandre
N1 - Funding Information:
S.C., A.T., and K.-R.M. thank the Deutsche Forschungsgemeinschaft, Germany (projects MU 987/20-1 and EXC 2046/1 [ID: 390685689] ) for funding this work. A.T. is funded by the European Research Council with ERC-CoG grant BeStMo. This work was supported by the German Ministry for Education and Research as Berlin Big Data Centre ( 01IS14013A ) and Berlin Center for Machine Learning ( 01IS18037I ). This work was also supported by the Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 ). This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the National Science Foundation, United States .
Funding Information:
S.C. A.T. and K.-R.M. thank the Deutsche Forschungsgemeinschaft, Germany (projects MU 987/20-1 and EXC 2046/1 [ID: 390685689]) for funding this work. A.T. is funded by the European Research Council with ERC-CoG grant BeStMo. This work was supported by the German Ministry for Education and Research as Berlin Big Data Centre (01IS14013A) and Berlin Center for Machine Learning (01IS18037I). This work was also supported by the Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451). This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the National Science Foundation, United States.
Funding Information:
S.C., A.T., and K.-R.M. thank the Deutsche Forschungsgemeinschaft, Germany (projects MU 987/20-1 and EXC 2046/1 [ID: 390685689]) for funding this work. A.T. is funded by the European Research Council with ERC-CoG grant BeStMo. This work was supported by the German Ministry for Education and Research as Berlin Big Data Centre (01IS14013A) and Berlin Center for Machine Learning (01IS18037I). This work was also supported by the Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451). This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the National Science Foundation, United States.
Publisher Copyright:
© 2019 The Author(s)
PY - 2019/7
Y1 - 2019/7
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
UR - http://www.scopus.com/inward/record.url?scp=85062624346&partnerID=8YFLogxK
U2 - 10.1016/j.cpc.2019.02.007
DO - 10.1016/j.cpc.2019.02.007
M3 - Article
AN - SCOPUS:85062624346
VL - 240
SP - 38
EP - 45
JO - Computer Physics Communications
JF - Computer Physics Communications
SN - 0010-4655
ER -