activity
20192022
most citedAb initio simulation of amorphous graphite

52 citations · 102 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2022★ 1 cited

Formation of amorphous carbon multi-walled nanotubes from random initial configurations

C. Ugwumadu, R. Thapa, Y. Al-Majali +2

Amorphous carbon nanotubes (a-CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gau…

cond-mat.mtrl-sci2022★ 11 cited

Atomistic Nature of Amorphous Graphite

C. Ugwumadu, K. Nepal, R. Thapa +1

This paper focuses on the structural, electronic, and vibrational features of amorphous graphite [R. Thapa , Phys. Rev. Lett. 128, 236402 (2022)]. The structure o…

cond-mat.dis-nn2022★ 18 cited

Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential

C. Ugwumadu, K. Nepal, R. Thapa +4

Multi-shell fullerenes "buckyonions" were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potentia…

cond-mat.mtrl-sci2022★ 52 cited

Ab initio simulation of amorphous graphite

Rajendra Thapa, Chinonso Ugwumadu, Kishor Nepal +2

A disorder-order transition from amorphous carbon (aC) to layered amorphous graphene (LAG) has been predicted using ab-initio methods. Amorphous carbon at densities close to the gr…

cond-mat.mtrl-sci2020★ 13 cited

Structure and charge transport of amorphous -doped : An ab initio study

Rajendra Thapa, Bishal Bhattarai, M. N. Kozicki +2

In this paper, we present ab initio computer models of Cu-doped amorphous Ta2O5 , a promising candidate for Conducting Bridge Random Access Memory (CBRAM) memory devices, and study…

cond-mat.mtrl-sci2019★ 7 cited

Ab initio inversion of structure and the lattice dynamics of a metallic glass: The case of Pd40Ni40P20

Bishal Bhattarai, Rajendra Thapa, David A. Drabold

In this paper we infer the structure of Pd40Ni40P20 from experimental diffraction data and ab initio interactions using Force Enhanced Atomic Refinement (FEAR). Our model accuratel…