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sGDML: Constructing accurate and data efficient molecular force fields using machine learning
sGDML: ساخت زمینه های نیروی مولکولی دقیق و کارآمد با استفاده از یادگیری ماشین-2019 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. Keywords: Machine learning potential | Machine learning force field | Ab initio molecular dynamics | Path integral molecular dynamics | Coupled cluster calculations | Molecular property prediction | Quantum chemistry | Gradient domain machine learning |
مقاله انگلیسی |
2 |
Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure
پیش بینی زلزله در کالیفرنیا با استفاده از الگوریتم های رگرسیون و زیرساخت های داده بزرگ مبتنی بر ابر-2018 Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades.
Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satis
factory results. In recent years, powerful computational techniques to analyze big data have emerged, making
possible the analysis of massive datasets. These new methods make use of physical resources like cloud based
architectures. California is known for being one of the regions with highest seismic activity in the world and many
data are available. In this work, the use of several regression algorithms combined with ensemble learning is
explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the
next seven days. Apache Spark framework, H2O library in R language and Amazon cloud infrastructure were been
used, reporting very promising results.
Keywords: Earthquake prediction ، Big data analytics ، Cluster computing ، Regression ، Ensemble learning |
مقاله انگلیسی |