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Autor:
Tan, Aik Rui
Neural network interatomic potentials (NNIPs) are a significant advancement in computational materials science and chemistry for their ability to accurately approximate the potential energy surface (PES) of atomic systems with significantly reduced c
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 (MLIP). However, the complexity of molecular systems, characterized by in
Externí odkaz:
http://arxiv.org/abs/2402.03753
Autor:
Tan, Aik Rui, Urata, Shingo, Goldman, Samuel, Dietschreit, Johannes C. B., Gómez-Bombarelli, Rafael
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 employed to model interatomic potentials in materials systems, this pr
Externí odkaz:
http://arxiv.org/abs/2305.01754
Autor:
Damewood, James, Karaguesian, Jessica, Lunger, Jaclyn R., Tan, Aik Rui, Xie, Mingrou, Peng, Jiayu, Gómez-Bombarelli, Rafael
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 composition, structure, and properties and by exploiting such relations for d
Externí odkaz:
http://arxiv.org/abs/2301.08813
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 hinders our ability to navigate structure-property relationships. For
Externí odkaz:
http://arxiv.org/abs/2111.07452
Publikováno v:
Nat. Commun. 12, 5104 (2021)
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within wel
Externí odkaz:
http://arxiv.org/abs/2101.11588
Publikováno v:
In Computational Materials Science 5 June 2023 225
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Akademický článek
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Publikováno v:
Journal of Chemical Physics; 8/21/2018, Vol. 149 Issue 7, pN.PAG-N.PAG, 17p, 6 Diagrams, 2 Charts, 3 Graphs