Výsledky vyhledávání - "Botu, Venkatesh"

Report

A study of adatom ripening on an Al (111) surface with machine learning force fields

Autor: Botu, Venkatesh, Chapman, James, Ramprasad, Rampi

Surface phenomena are increasingly becoming important in exploring nanoscale materials growth and characterization. Consequently, the need for atomistic based simulations is increasing. Nevertheless, relying entirely on quantum mechanical methods lim

Externí odkaz: http://arxiv.org/abs/1610.04684

Zobrazit plný text záznamu

Report

Machine learning force fields: Construction, validation, and outlook

Autor: Botu, Venkatesh, Batra, Rohit, Chapman, James, Ramprasad, Rampi

Force fields developed with machine learning methods in tandem with quantum mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy, and (iii) versatility. Recently, we proposed one such approach, wherein, the vectorial force o

Externí odkaz: http://arxiv.org/abs/1610.02098

Zobrazit plný text záznamu

Report

A learning scheme to predict atomic forces and accelerate materials simulations

Autor: Botu, Venkatesh, Ramprasad, Rampi

The behavior of an atom in a molecule, liquid or solid is governed by the force it experiences. If the dependence of this vectorial force on the atomic chemical environment can be $learned$ efficiently with high-fidelity from benchmark reference resu

Externí odkaz: http://arxiv.org/abs/1505.02701

Zobrazit plný text záznamu

Report

Ab-Initio Molecular Dynamics Acceleration Scheme with an Adaptive Machine Learning Framework

Autor: Botu, Venkatesh, Ramprasad, Rampi

Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in such simu

Externí odkaz: http://arxiv.org/abs/1410.3353

Zobrazit plný text záznamu