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Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)

Autor: Qiu, Jiaxing, Lake, Douglas E., Chernyavskiy, Pavel, Henry, Teague R.

For prediction models developed on clustered data that do not account for cluster heterogeneity in model parameterization, it is crucial to use cluster-based validation to assess model generalizability on unseen clusters. This paper introduces a clus

Externí odkaz: http://arxiv.org/abs/2405.20400

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Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics

Autor: Ghaffari, Kimia, Bavdekar, Salil, Spearot, Douglas E., Subhash, Ghatu

The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-develop

Externí odkaz: http://arxiv.org/abs/2402.05222

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Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database

Autor: Roberts, Josiah, Rijal, Biswas, Divilov, Simon, Maria, Jon-Paul, Fahrenholtz, William G., Wolfe, Douglas E., Brenner, Donald W., Curtarolo, Stefano, Zurek, Eva

Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW dat

Externí odkaz: http://arxiv.org/abs/2401.01852

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