Machine learning for multi-fidelity scale bridging and dynamical simulations of materials

Abstract

Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ranging from the high fidelity albeit computationally expensive ab-initio MD to relatively lower fidelity but much more efficient classical MD such as atomistic and coarse-grained models. Each of these different flavors of MD have been independently used by materials scientists to bring about breakthroughs in materials discovery and design. A significant gulf exists between the various MD flavors, each having varying levels of fidelity. The accuracy of DFT or ab-initio MD is generally much higher than that of classical atomistic simulations which is higher than that of coarse-grained models. Multi-fidelity scale bridging to combine the accuracy and flexibility of ab-initio MD with efficiency classical MD has been a longstanding goal. The advent of big-data analytics has brought to the forefront powerful machine learning methods that can be deployed to achieve this goal. Here, we provide our perspective on the challenges in multi-fidelity scale bridging and trace the developments leading up to the use of machine learning algorithms and data-science towards addressing this grand challenge. arXiv:2004.00232v1 [physics.comp-ph] 1 Apr 2020

Molecular Dynamics and Machine Learning in Catalysts

“…Machine learning potential is one of the most critical calculations advances in recent years and has been intensively studied and applied in catalysis [112][113][114][115][116][117][118][119][120][121][122][123][124][125][126][127]. The machine learning potential is a method that uses the machine learning algorithm to find the underneath relationship of the atomic configuration and energy [128].…”

A survey of multiscale modeling: Foundations, historical milestones, current status, and future prospects

“…Advances in ML-derived force fields are promising to revolutionize classical simulations by directly defining energy landscapes from more accurate QM simulations. 188,189 Besides, in particle-based simulations of MSM, the efficient sampling of high-dimensional conformational spaces constitutes a significant challenge in the computational molecular sciences limiting the longtime MD simulations of molecular systems in biophysical chemistry and materials science. Combining MD simulations with ML can provide a powerful approach to address the challenges mentioned above.…”

Multiscale modeling: foundations, historical milestones, current status, and future prospects

“…Advances in ML-derived force fields are promising to revolutionize classical simulations by directly defining energy landscapes from more accurate quantum mechanical simulations [159,160]. Besides, in particlebased simulations of MSM, the efficient sampling of high-dimensional conformational spaces constitutes a significant challenge in the computational molecular sciences limiting the longtime molecular dynamics (MD) simulations of molecular systems in biophysical chemistry and materials science.…”