AIMC Topic: Nuclear Energy

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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one Sep 2, 2021
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula...
Temperature Deep Learning Proteins Molecular Dynamics Simulation Crystallization Protein Structure, Tertiary Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Nuclear Energy
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Reverse graph self-attention for target-directed atomic importance estimation.

Neural networks : the official journal of the International Neural Network Society Oct 8, 2020
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and materials science. The most common way to estimate the atomic importance is to compute the electronic structure usin...
Humans Neural Networks, Computer Machine Learning Electronics Attention Molecular Conformation Nuclear Energy
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