94 citations · 182 across the 5 of their papers we have counts for
5 papers
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gomez-Bombarelli
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (ML…
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Aik Rui Tan, Shingo Urata, Samuel Goldman +2
Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are…
Representations of Materials for Machine Learning
James Damewood, Jessica Karaguesian, Jaclyn R. Lunger +4
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composi…
Graph theory-based structural analysis on density anomaly of silica glass
Aik Rui Tan, Shingo Urata, Masatsugu Yamada +1
Analyzing the atomic structure of glassy materials is a tremendous challenge both experimentally and computationally, and the lack of direct, detailed insights into glass structure…
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce t…