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pro vyhledávání: '"Johannes, C. B."'
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
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an e
Externí odkaz:
http://arxiv.org/abs/2402.01542
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
The description of chemical processes at the molecular level is often facilitated by use of reaction coordinates, or collective variables (CVs). The CV measures the progress of the reaction and allows the construction of profiles that track the evolu
Externí odkaz:
http://arxiv.org/abs/2304.10676
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs). We propose using differentiable simul
Externí odkaz:
http://arxiv.org/abs/2301.03480
Autor:
Dietschreit, Johannes C. B., Diestler, Dennis J., Hulm, Andreas, Ochsenfeld, Christian, Gómez-Bombarelli, Rafael
Publikováno v:
J. Chem. Phys. 157, 084113 (2022)
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) that discriminates between R and P, one can define a free-energy profile (FEP) as the logarithm of the marginal Bo
Externí odkaz:
http://arxiv.org/abs/2206.02893
Autor:
Aik Rui Tan, Shingo Urata, Samuel Goldman, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-11 (2023)
Abstract 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
Externí odkaz:
https://doaj.org/article/677a1abb88db403294eb8bb981b914cb
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Akademický článek
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Publikováno v:
Journal of Chemical Physics; 1/28/2023, Vol. 158 Issue 4, p1-12, 12p