253 research outputs found
Tilt-induced charge localisation in phosphide antiperovskite photovoltaics
Antiperovskites are a rich family of compounds with applications in battery
cathodes, superconductors, solid-state lighting, and catalysis. Recently, a
novel series of antimonide phosphide antiperovskites (ASbP, where A = Ca,
Sr, Ba) were proposed as candidate photovoltaic absorbers due to their ideal
band gaps, small effective masses and strong optical absorption. In this work,
we explore this series of compounds in more detail using relativistic hybrid
density functional theory. We reveal that the proposed cubic structures are
dynamically unstable and instead identify a tilted orthorhombic Pnma phase as
the ground state. Tilting is shown to induce charge localisation that widens
the band gap and increases the effective masses. Despite this, we demonstrate
that the predicted maximum photovoltaic efficiencies remain high (24-31% for
200 nm thin films) by bringing the band gaps into the ideal range for a solar
absorber. Finally, we assess the band alignment of the series and suggest hole
and electron contact materials for efficient photovoltaic devices
Learning Generative ConvNets via Multi-grid Modeling and Sampling
This paper proposes a multi-grid method for learning energy-based generative
ConvNet models of images. For each grid, we learn an energy-based probabilistic
model where the energy function is defined by a bottom-up convolutional neural
network (ConvNet or CNN). Learning such a model requires generating synthesized
examples from the model. Within each iteration of our learning algorithm, for
each observed training image, we generate synthesized images at multiple grids
by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of
the training image. The synthesized image at each subsequent grid is obtained
by a finite-step MCMC initialized from the synthesized image generated at the
previous coarser grid. After obtaining the synthesized examples, the parameters
of the models at multiple grids are updated separately and simultaneously based
on the differences between synthesized and observed examples. We show that this
multi-grid method can learn realistic energy-based generative ConvNet models,
and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
Training energy-based models (EBMs) with maximum likelihood estimation on
high-dimensional data can be both challenging and time-consuming. As a result,
there a noticeable gap in sample quality between EBMs and other generative
frameworks like GANs and diffusion models. To close this gap, inspired by the
recent efforts of learning EBMs by maximimizing diffusion recovery likelihood
(DRL), we propose cooperative diffusion recovery likelihood (CDRL), an
effective approach to tractably learn and sample from a series of EBMs defined
on increasingly noisy versons of a dataset, paired with an initializer model
for each EBM. At each noise level, the initializer model learns to amortize the
sampling process of the EBM, and the two models are jointly estimated within a
cooperative training framework. Samples from the initializer serve as starting
points that are refined by a few sampling steps from the EBM. With the refined
samples, the EBM is optimized by maximizing recovery likelihood, while the
initializer is optimized by learning from the difference between the refined
samples and the initial samples. We develop a new noise schedule and a variance
reduction technique to further improve the sample quality. Combining these
advances, we significantly boost the FID scores compared to existing EBM
methods on CIFAR-10 and ImageNet 32x32, with a 2x speedup over DRL. In
addition, we extend our method to compositional generation and image inpainting
tasks, and showcase the compatibility of CDRL with classifier-free guidance for
conditional generation, achieving similar trade-offs between sample quality and
sample diversity as in diffusion models
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