Výsledky vyhledávání - "Molinari, Nicola"

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Uncertainty Driven Active Learning of Coarse Grained Free Energy Models

Autor: Duschatko, Blake R., Vandermause, Jonathan, Molinari, Nicola, Kozinsky, Boris

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all

Externí odkaz: http://arxiv.org/abs/2210.16364

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Salt-in-Ionic-Liquid Electrolytes: Ion Network Formation and Negative Effective Charges of Alkali Metal Cations

Autor: McEldrew, Michael, Goodwin, Zachary A. H., Molinari, Nicola, Kozinsky, Boris, Kornyshev, Alexei A., Bazant, Martin Z.

Salt-in-ionic liquid electrolytes have attracted significant attention as potential electrolytes for next generation batteries largely due to their safety enhancements over typical organic electrolytes. However, recent experimental and computational

Externí odkaz: http://arxiv.org/abs/2106.12492

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E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

Autor: Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P., Kornbluth, Mordechai, Molinari, Nicola, Smidt, Tess E., Kozinsky, Boris

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aw

Externí odkaz: http://arxiv.org/abs/2101.03164

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Spectral denoising for unsupervised analysis of correlated ionic transport

Autor: Molinari, Nicola, Xie, Yu, Leifer, Ian, Marcolongo, Aris, Kornbluth, Mordechai, Kozinsky, Boris

Publikováno v: Phys. Rev. Lett. 127, 025901 (2021)

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displa

Externí odkaz: http://arxiv.org/abs/2007.08734

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Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems

Autor: Mailoa, Jonathan P., Kornbluth, Mordechai, Batzner, Simon L., Samsonidze, Georgy, Lam, Stephen T., Ablitt, Chris, Molinari, Nicola, Kozinsky, Boris

Publikováno v: Nature Machine Intelligence 1 (2019)

Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations.

Externí odkaz: http://arxiv.org/abs/1905.02791

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