arXiv subjects

Ankit Jain

Publications and source records attributed to Ankit Jain.

26 records · Page 2Linked to original sources

arXiv2021

Anharmonic lattice dynamics and thermal transport in type-I inorganic clathrates

The anharmonic phonon properties of type-I filled inorganic clathrates Ba8 Ga16 Ge30 and Sr8 Ga16 Ge30 are obtained from the first-principles calculations by considering the temperature-dependent sampling of the potential energy surface and quartic phonon renormalization. Owing to the weak binding of guest atoms with the host lattice, the obtained guest modes undergo strong renormalization with temperature and become stiffer by up to 50% at room temperature in Sr8Ga16Ge30. The calculated phonon frequencies and associated thermal mean squared displacements are comparable with experiments despite the on-centering of guest atoms at cage centers in both clathrates. Lattice thermal conductivities are obtained in the temperature range of 50- 300 K accounting for three-phonon scattering processes and multi-channel thermal transport. The contribution of coherent transport channel is significant at room temperature (13% and 22% in Ba8Ga16Ge30 and Sr8Ga16Ge30) but is insufficient to explain the experimentally observed glass-like thermal transport in Sr8Ga16Ge30.

cond-mat.mtrl-sci↗

arXiv2021

Density functional theory driven phononic thermal conductivity prediction of biphenylene: A comparison with graphene

The thermal transport properties of biphenylene network (BPN), a novel sp2 -hybridized two-dimensional allotrope of carbon atoms recently realized in experiments [Fan et al., Science, 372 852-856 (2021)], are studied using the density functional theory-driven solution of the Boltzmann transport equation. The thermal transport in BPN is anisotropic and the obtained thermal conductivities are more than an order of magnitude lower than that in graphene, despite similar sp2-hybridized planar-structure of both allotropes. The lower thermal conductivity in BPN is found to originate from enhanced anharmonicity which in turn is a result of reduced crystal symmetry of BPN.

cond-mat.mtrl-sci↗

arXiv2020

Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. This formulation decouples actions from values while still learning off-policy. We highlight the benefits of this approach in terms of value function transfer, learning within redundant action spaces, and learning off-policy from state observations generated by sub-optimal or completely random policies. Code and videos are available at http://sites.google.com/view/qss-paper.

cs.LG↗

arXiv2020

Meta-Graph: Few Shot Link Prediction via Meta Learning

We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new graph after a small amount of training. We show that current link prediction methods are generally ill-equipped to handle this task. They cannot effectively transfer learned knowledge from one graph to another and are unable to effectively learn from sparse samples of edges. To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph. Our framework leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization. Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph can learn to quickly adapt to a new graph using only a small sample of true edges, enabling not only fast adaptation but also improved results at convergence.

cs.LG↗

arXiv2018

Atomic positions independent descriptor for machine learning of material properties

The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not available {\it a priori} for new materials which severely limits exploration of novel materials. We overcome this limitation by using only crystallographic symmetry information in the structural description of materials. We show that for materials with identical structural symmetry, machine learning is trivial and accuracies similar to that of density functional theory calculations can be achieved by using only atomic numbers in the material description. For machine learning of formation energies of bulk crystalline solids, this simple material descriptor is able to achieve prediction mean absolute errors of only 0.07 eV/atom on a test dataset consisting of more than 85,000 diverse materials. This atomic-position independent material descriptor presents a new route of materials discovery wherein millions of materials can be screened by training a machine learning model over a drastically reduced subspace of materials.

cond-mat.mtrl-sci↗

arXiv2016

Lattice dynamics and the nature of structural transitions in organolead halide perovskites

Organolead halide perovskites are a family of hybrid organic-inorganic compounds whose remarkable optoelectronic properties have been under intensive scrutiny in recent years. Here we use inelastic X-ray scattering to study low-energy lattice excitations in single crystals of methylammonium lead iodide and bromide perovskites. Our findings confirm the displacive nature of the cubic-to-tetragonal phase transition, which is further shown, using neutron and x-ray diffraction, to be close to a tricritical point. Lastly, we detect quasistatic symmetry-breaking nanodomains persisting well into the high-temperature cubic phase, possibly stabilized by local defects. These findings reveal key structural properties of these materials, and also bear important implications for carrier dynamics across an extended temperature range relevant for photovoltaic applications.

cond-mat.mtrl-sci↗

arXiv2013

Prospects of Hysteresis-Free Abrupt Switching (0mV/dec) in Landau Switches

Sub-threshold swing (S) defines the sharpness of ON-OFF switching of a Field Effect Transistor (FET) with S=0 corresponding to abrupt switching characteristics. While thermodynamics dictates S to be greater than or equal to 60mV/dec for classical FETs, "Landau switches" use inherently unstable gate insulators to achieve abrupt switching. Unfortunately, S=0 switching is always achieved at the expense of an intrinsic hysteresis, making these switches unsuitable for low-power applications. The fundamental question therefore remains: Under what condition, hysteresis-free abrupt switching can be achieved in a Landau switch? In this paper, we first provide an intuitive classification of all charge based switches in terms of their energy landscapes and identify two-well energy landscape as the characteristic feature of Landau switches. We then use nanoelectromechanical field effect transistor (NEMFET) as an illustrative example of a Landau switch and conclude that a flat energy landscape is essential for hysteresis-free abrupt switching. In contrast, a hysteresis-free smooth sub-60mV/dec switching is obtained by stabilizing the unstable gate insulator in its unstable regime. Our conclusions have broad implications and may considerably simplify the design of next charge based logic switch.

cond-mat.mes-hall↗

arXiv2013

Emerging Ideas in Nanocantilever based Biological Sensors

In this review article, we focus on emerging nanocantilever based biological sensors and discuss the response of nanocantilevers towards bio-molecules capture. The article guides the reader through various modes of operation (e.g., static or dynamic) to detect the change in characteristics (e.g., mass, stiffness, and/or surface stress) of cantilever due to adsorption of bio-molecules on cantilever surface. First, we explain the classical linear resonant mode mass sensors and static stress based sensors. The effect of operating the cantilever in nonlinear regime is then illustrated through examples of bifurcation based mass sensors and electromechanical coupling based Flexure-FET biosensors. We believe that a new class of nonlinear sensors, with their extraordinary sensitivity towards bio-molecules capture, could be the potential candidate for low cost point-of-care applications.

physics.ins-det↗