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Roberto Car

Researcher at Princeton University

Publications -  406
Citations -  90989

Roberto Car is an academic researcher from Princeton University. The author has contributed to research in topics: Density functional theory & Ab initio. The author has an hindex of 99, co-authored 389 publications receiving 76681 citations. Previous affiliations of Roberto Car include International School for Advanced Studies & University of Geneva.

Papers
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Journal ArticleDOI

Defect structure and dynamics in silicon.

TL;DR: In this paper, the authors give a brief account of recent calculations of equilibrium configurations, formation energies, and migration energies of intrinsic lattice defects (vacancies, self-interstitials) and complexes of dopant impurities (phosphorus, aluminum) with these defects.
Posted Content

A favorably-scaling natural-orbital functional theory based on higher-order occupation probabilities

TL;DR: In this paper, a novel energy functional for ground-state electronic-structure calculations is proposed. The functional derives from a sequence of controlled approximations to the two-particle density matrix.

Thermal Conductivity of Water at Extreme Conditions

TL;DR: In this article , a deep neural network potential fitted to density functional theory data was used to estimate the thermal conductivity of water at extreme conditions, and the authors found that the thermal properties of water are weakly dependent on temperature and monotonically increase with pressure with an approximate square root behavior.
Book ChapterDOI

Ab-Initio Study of Amorphous and Liquid Carbon

TL;DR: In this article, the properties of both non-crystalline and liquid carbon are investigated, in particular, the structural and electronic properties of the low density disordered phases, the nature of the liquid state and the melting mechanism.

Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point

TL;DR: In this article , the authors explore the possibility of a liquid-liquid critical point in deeply supercooled water using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations.