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Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
April 17, 2024, 4:43 a.m. | Juno Nam, Rafael G\'omez-Bombarelli
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs …
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