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Found 8 papers, 6 papers with code
ZnTrack -- Data as Code
The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon.
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Existing biased and unbiased MD simulations, however, are prone to miss either rare events or extrapolative regions -- areas of the configurational space where unreliable predictions are made.
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
We propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor, based on the Gaussian-moment neural-network approach.
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials.
Transfer learning for chemically accurate interatomic neural network potentials
This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets.
A Framework and Benchmark for Deep Batch Active Learning for Regression
We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simulations with almost ab initio accuracy.
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical potentials.
