Výsledky vyhledávání - "Smith, Daniel G A"

Report

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Autor: Christensen, Anders S., Sirumalla, Sai Krishna, Qiao, Zhuoran, O'Connor, Michael B., Smith, Daniel G. A., Ding, Feizhi, Bygrave, Peter J., Anandkumar, Animashree, Welborn, Matthew, Manby, Frederick R., Miller III, Thomas F.

We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing neural network that uses symmet

Externí odkaz: http://arxiv.org/abs/2107.00299

Zobrazit plný text záznamu

Report

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

Autor: Qiao, Zhuoran, Ding, Feizhi, Welborn, Matthew, Bygrave, Peter J., Smith, Daniel G. A., Anandkumar, Animashree, Manby, Frederick R., Miller III, Thomas F.

We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbit

Externí odkaz: http://arxiv.org/abs/2011.02680

Zobrazit plný text záznamu

Report

First-order symmetry-adapted perturbation theory for multiplet splittings

Autor: Patkowski, Konrad, Żuchowski, Piotr S., Smith, Daniel G. A.

We present a symmetry-adapted perturbation theory (SAPT) for the interaction of two high-spin open-shell molecules (described by their restricted open-shell Hartree-Fock determinants) resulting in low-spin states of the complex. The previously availa

Externí odkaz: http://arxiv.org/abs/1801.04078

Zobrazit plný text záznamu