Physics-informed machine learning potential for nanoparticle self-assembly: 3000x faster than explicit simulation. Permutationally invariant polynomials fitted to CG MD potentials of mean force. Reproduces 1D strings, 2D sheets, 3D aggregates. npj Computational Materials 2023.
What is the yilong-sim/many-body-nanoparticle-potential-ml GitHub project? Description: "Physics-informed machine learning potential for nanoparticle self-assembly: 3000x faster than explicit simulation. Permutationally invariant polynomials fitted to CG MD potentials of mean force. Reproduces 1D strings, 2D sheets, 3D aggregates. npj Computational Materials 2023.". Written in Jupyter Notebook. Explain what it does, its main use cases, key features, and who would benefit from using it.
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