Physical Review B
01 Jan 2011-
About: The article was published on 2011-01-01 and is currently open access. It has received 2117 citations till now.
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TL;DR: The pymatgen library as mentioned in this paper is an open-source Python library for materials analysis that provides a well-tested set of structure and thermodynamic analyses relevant to many applications, and an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments.
2,364 citations
Université catholique de Louvain1, Centre national de la recherche scientifique2, Université de Montréal3, French Alternative Energies and Atomic Energy Commission4, École normale supérieure de Lyon5, European Synchrotron Radiation Facility6, University of Liège7, University of Basel8, Université de Namur9, University of Milan10, Spanish National Research Council11, Dalhousie University12
TL;DR: The present paper provides an exhaustive account of the capabilities of ABINIT, with adequate references to the underlying theory, as well as the relevant input variables, tests and, if available, ABinIT tutorials.
2,226 citations
TL;DR: The trivalent europium ion (Eu3+) is well known for its strong luminescence in the red spectral region, but this ion is also interesting from a theoretical point of view as mentioned in this paper.
1,906 citations
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TL;DR: In this paper, a method to disperse and exfoliate graphite to give graphene suspended in water-surfactant solutions was demonstrated. Optical characterisation of these suspensions allowed the partial optimisation of the dispersion process and showed the dispersed phase to consist of small graphitic flakes.
Abstract: We have demonstrated a method to disperse and exfoliate graphite to give graphene suspended in water-surfactant solutions. Optical characterisation of these suspensions allowed the partial optimisation of the dispersion process. Transmission electron microscopy showed the dispersed phase to consist of small graphitic flakes. More than 40% of these flakes had <5 layers with ~3% of flakes consisting of monolayers. These flakes are stabilised against reaggregation by Coulomb repulsion due to the adsorbed surfactant. However, the larger flakes tend to sediment out over ~6 weeks, leaving only small flakes dispersed. It is possible to form thin films by vacuum filtration of these dispersions. Raman and IR spectroscopic analysis of these films suggests the flakes to be largely free of defects and oxides. The deposited films are reasonably conductive and are semi-transparent. Further improvements may result in the development of cheap transparent conductors.
1,803 citations
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Abstract: Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
1,504 citations
References
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TL;DR: The pymatgen library as mentioned in this paper is an open-source Python library for materials analysis that provides a well-tested set of structure and thermodynamic analyses relevant to many applications, and an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments.
2,364 citations
Université catholique de Louvain1, Centre national de la recherche scientifique2, Université de Montréal3, French Alternative Energies and Atomic Energy Commission4, École normale supérieure de Lyon5, European Synchrotron Radiation Facility6, University of Liège7, University of Basel8, Université de Namur9, University of Milan10, Spanish National Research Council11, Dalhousie University12
TL;DR: The present paper provides an exhaustive account of the capabilities of ABINIT, with adequate references to the underlying theory, as well as the relevant input variables, tests and, if available, ABinIT tutorials.
2,226 citations
TL;DR: A method to disperse and exfoliate graphite to give graphene suspended in water-surfactant solutions and suggests the flakes to be largely free of defects and oxides, although X-ray photoelectron spectroscopy shows evidence of a small oxide population.
Abstract: We have demonstrated a method to disperse and exfoliate graphite to give graphene suspended in water−surfactant solutions. Optical characterization of these suspensions allowed the partial optimization of the dispersion process. Transmission electron microscopy showed the dispersed phase to consist of small graphitic flakes. More than 40% of these flakes had <5 layers with ∼3% of flakes consisting of monolayers. Atomic resolution transmission electron microscopy shows the monolayers to be generally free of defects. The dispersed graphitic flakes are stabilized against reaggregation by Coulomb repulsion due to the adsorbed surfactant. We use DLVO and Hamaker theory to describe this stabilization. However, the larger flakes tend to sediment out over ∼6 weeks, leaving only small flakes dispersed. It is possible to form thin films by vacuum filtration of these dispersions. Raman and IR spectroscopic analysis of these films suggests the flakes to be largely free of defects and oxides, although X-ray photoelect...
2,086 citations
TL;DR: The trivalent europium ion (Eu3+) is well known for its strong luminescence in the red spectral region, but this ion is also interesting from a theoretical point of view as mentioned in this paper.
1,906 citations
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Abstract: Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
1,504 citations