Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
Institute of Informatics and Telecommunications（信息学与电信研究所）；Institute of Nanoscience and Nanotechnology（纳米科学与纳米技术研究所）；National Centre for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）；SciFY P.N.P.C.（SciFY公共非营利公司）；School of Chemical Engineering（化学工程学院）；National Technical University of Athens（雅典国立技术大学）
Eleonora Ricci, George Giannakopoulos, Vangelis Karkaletsis, Doros N. Theodorou, Niki Vergadou
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Proceedings of 12th Conference on Artificial Intelligence (SETN), 2022
Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.
NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）；Suzhou Laboratory（苏州实验室）；Key Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University（吉林大学物理学院材料模拟方法与软件教育部重点实验室）
Understanding Dynamics in Coarse-Grained Models: IV. Connection of Fine-Grained and Coarse-Grained Dynamics with the Stokes-Einstein and Stokes-Einstein-Debye Relations
