Semiconductor to metal transition in bilayer phosphorene under normal compressive strain

Phosphorene, a two-dimensional (2D) analog of black phosphorous, has been a subject of immense interest recently, due to its high carrier mobilities and a tunable bandgap. So far, tunability has been predicted to be obtained with very high compressive/tensile in-plane strains, and vertical electric field, which are difficult to achieve experimentally. Here, we show using density functional theory based calculations the possibility of tuning electronic properties by applying normal compressive strain in bilayer phosphorene. A complete and fully reversible semiconductor to metal transition has been observed at $\sim13.35\%$ strain, which can be easily realized experimentally. Furthermore, a direct to indirect bandgap transition has also been observed at $\sim3\%$ strain, which is a signature of unique band-gap modulation pattern in this material. The absence of negative frequencies in phonon spectra as a function of strain demonstrates the structural integrity of the sheets at relatively higher strain range. The carrier mobilities and effective masses also do not change significantly as a function of strain, keeping the transport properties nearly unchanged. This inherent ease of tunability of electronic properties without affecting the excellent transport properties of phosphorene sheets is expected to pave way for further fundamental research leading to phosphorene-based multi-physics devices.Comment: 12 pages, 5 figure

Know your audience: specializing grounded language models with listener subtraction

Effective communication requires adapting to the idiosyncrasies of each communicative context--such as the common ground shared with each partner. Humans demonstrate this ability to specialize to their audience in many contexts, such as the popular game Dixit. We take inspiration from Dixit to formulate a multi-agent image reference game where a (trained) speaker model is rewarded for describing a target image such that one (pretrained) listener model can correctly identify it among distractors, but another listener cannot. To adapt, the speaker must exploit differences in the knowledge it shares with the different listeners. We show that finetuning an attention-based adapter between a CLIP vision encoder and a large language model in this contrastive, multi-agent setting gives rise to context-dependent natural language specialization from rewards only, without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communication.Comment: 28 pages, 9 figure

Know your audience: specializing grounded language models with listener subtraction

Effective communication requires adapting to the idiosyncrasies of each communicative context—such as the common ground shared with each partner. Humans demonstrate this ability to specialize to their audience in many contexts, such as the popular game Dixit. We take inspiration from Dixit to formulate a multiagent image reference game where a (trained) speaker model is rewarded for describing a target image such that one (pretrained) listener model can correctly identify it among distractors, but another listener cannot. To adapt, the speaker must exploit differences in the knowledge it shares with the different listeners. We show that finetuning an attention-based adapter between a CLIP vision encoder and a large language model in this contrastive, multi-agent setting gives rise to context-dependent natural language specialization from rewards only, without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communicatio

Confronting Reward Model Overoptimization with Constrained RLHF

Large language models are typically aligned with human preferences by optimizing $\textit{reward models}$ (RMs) fitted to human feedback. However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality. This itself presents a challenge, as it is difficult to appropriately weight these component RMs when combining them. Compounding this difficulty, because any RM is only a proxy for human evaluation, this process is vulnerable to $\textit{overoptimization}$, wherein past a certain point, accumulating higher reward is associated with worse human ratings. In this paper, we perform, to our knowledge, the first study on overoptimization in composite RMs, showing that correlation between component RMs has a significant effect on the locations of these points. We then introduce an approach to solve this issue using constrained reinforcement learning as a means of preventing the agent from exceeding each RM's threshold of usefulness. Our method addresses the problem of weighting component RMs by learning dynamic weights, naturally expressed by Lagrange multipliers. As a result, each RM stays within the range at which it is an effective proxy, improving evaluation performance. Finally, we introduce an adaptive method using gradient-free optimization to identify and optimize towards these points during a single run

Engineering Defect Transition-Levels through the van der Waals Heterostructure

Tuning defect levels in 2D semiconductors without significantly altering the integrity of the materials remains one of the most difficult challenges, which critically restricts their usage in electronic and optoelectronic devices. In this study, we demonstrate that the deep levels created by a cation vacancy in a monolayer of MoS2 can be tuned to a shallow level by heterostructuring it with a monolayer of WS2, while maintaining their structural and compositional integrity intact. The overall change in dielectric constant rescales the defect transition levels in a heterostructure. As a result, the deep defect levels are shallowed by nearly 4 (VTMo-1) and 2 (V-w(-1)) times, respectively, compared to their monolayer counterparts. Our finding has the potential to revolutionize the doping strategy of the 2D materials and could pave the way for 2D electronics

Low formation energy and kinetic barrier of Stone-Wales defect in infinite and finite silicene

Stone-Wales (SW) defects in materials having hexagonal lattice are the most common topological defects that affect the electronic and mechanical properties. Using first principles density functional theory based calculations, we study the formation energy and kinetic barrier of SW-defect in infinite and finite sheets of silicene. The formation energies as well as the barriers in both the cases are significantly lower than those of graphene. Furthermore, compared with the infinite sheets, the energy barriers and formation energies are lower for finite sheets. However, due to low barriers these defects are expected to heal out of the finite sheets. (C) 2013 Elsevier B.V. All rights reserved

Review of distribution of earnings statistics

Labour market theme. Includes bibliographical references. Title from coverSIGLEAvailable from British Library Document Supply Centre- DSC:6180. 57962(no 14) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Simultaneous tunability of the electronic and phononic gaps in SnS2 under normal compressive strain

Controlled variation of the electronic properties of. two-dimensional (2D) materials by applying strain has emerged as a promising way to design materials for customized applications. Using density functional theory (DFT) calculations, we show that while the electronic structure and indirect band gap of SnS2 do not change significantly with the number of layers, they can be reversibly tuned by applying biaxial tensile (BT), biaxial compressive (BC), and normal compressive (NC) strains. Mono to multilayered SnS2 exhibit a reversible semiconductor to metal (S-M) transition with applied strain. For bilayer (2L) SnS2, the S-Mtransition occurs at the strain values of 17%,-26%, and -24% under BT, BC, and NC strains, respectively. Due to weaker interlayer coupling, the critical strain value required to achieve the S-Mtransition in SnS2 under NC strain is much higher than for MoS2. From a stability viewpoint, SnS2 becomes unstable at very low strain values on applying BC (-6.5%) and BT strains (4.9%), while it is stable even up to the transition point (-24%) in the case of NC strain. In addition to the reversible tuning of the electronic properties of SnS2, we also show tunability in the phononic band gap of SnS2, which increases with applied NC strain. This gap increases three times faster than for MoS2. This simultaneous tunability of SnS2 at the electronic and phononic levels with strain, makes it a potential candidate in field effect transistors (FETs) and sensors as well as frequency filter applications

Mechanical and electronic properties of pristine and Ni-doped Si, Ge, and Sn sheets

Silicene, a graphene analogue of silicon, has been generating immense interest due to its potential for applications in miniaturized devices. Unlike planar graphene, silicene prefers a buckled structure. Here we explore the possibility of stabilizing the planar form of silicene by Ni doping using first principles density functional theory based calculations. It is found that planar as well as buckled structure is stable for Ni-doped silicene, but the buckled sheet has slightly lower total energy. The planar silicene sheet has unstable phonon modes. A comparative study of the mechanical properties reveals that the in-plane stiffness of both the pristine and the doped planar silicene is higher compared to that of the buckled silicene. This suggests that planar silicene is mechanically more robust. Electronic structure calculations of the planar and buckled Ni-doped silicene show that the energy bands at the Dirac point transform from linear behavior to parabolic dispersion. Furthermore, we extend our study to Ge and Sn sheets that are also stable and the trends of comparable mechanical stability of the planar and buckled phases remain the same

Vacancy mediated clipping of multi-layered graphene: A precursor for 1, 2 and 3D carbon structures

Using first principles calculations, we show that the overlapping defects in bi-layer graphene (both AA-and AB-stacked) interact forming inter-layer covalent bonds, giving rise to two-dimensional (2D) clipped structures, without explicit use of functional groups. These clipped structures can be transformed into one-dimensional (1D) double wall nanotubes (DWCNT) or multi-layered three dimensional (3D) bulk structures. These clipped structures show good mechanical strength due to covalent bonding between multi-layers. Clipping also provides a unique way to simultaneously harness the conductivity of both walls of a double wall nanotube through covalently bonded scattering junctions. With additional conducting channels and improved mechanical stability, these clipped structures can lead to a myriad of applications in novel devices. (C) 2015 Elsevier Ltd. All rights reserved