717 research outputs found
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An unprecedented 2D covalent organic framework with an htb net topology.
A 2D imine-linked COF with a hitherto unreported htb type topology was synthesized from a linear diamine linker and a judiciously designed tetra-aldehyde building block. This work opens the door to the development of COFs with unprecedented topologies and may broaden the scope of COF functional materials by pore size and pore surface engineering
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
In this paper, we consider non-convex multi-block bilevel optimization (MBBO)
problems, which involve lower level problems and have important
applications in machine learning. Designing a stochastic gradient and
controlling its variance is more intricate due to the hierarchical sampling of
blocks and data and the unique challenge of estimating hyper-gradient. We aim
to achieve three nice properties for our algorithm: (a) matching the
state-of-the-art complexity of standard BO problems with a single block; (b)
achieving parallel speedup by sampling blocks and sampling samples for
each sampled block per-iteration; (c) avoiding the computation of the inverse
of a high-dimensional Hessian matrix estimator. However, it is non-trivial to
achieve all of these by observing that existing works only achieve one or two
of these properties. To address the involved challenges for achieving (a, b,
c), we propose two stochastic algorithms by using advanced blockwise
variance-reduction techniques for tracking the Hessian matrices (for
low-dimensional problems) or the Hessian-vector products (for high-dimensional
problems), and prove an iteration complexity of
for finding an -stationary point
under appropriate conditions. We also conduct experiments to verify the
effectiveness of the proposed algorithms comparing with existing MBBO
algorithms
Deep Variational Free Energy Approach to Dense Hydrogen
We developed a deep generative model-based variational free energy approach
to the equations of state of dense hydrogen. We employ a normalizing flow
network to model the proton Boltzmann distribution and a fermionic neural
network to model the electron wave function at given proton positions. By
jointly optimizing the two neural networks we reached a comparable variational
free energy to the previous coupled electron-ion Monte Carlo calculation. The
predicted equation of state of dense hydrogen under planetary conditions is
denser than the findings of ab initio molecular dynamics calculation and
empirical chemical model. Moreover, direct access to the entropy and free
energy of dense hydrogen opens new opportunities in planetary modeling and
high-pressure physics research.Comment: 7+5 pages, 3+4 figures, code: https://github.com/fermiflow/hydroge
A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States
Ship roll motion in high sea states has large amplitudes and nonlinear
dynamics, and its prediction is significant for operability, safety, and
survivability. This paper presents a novel data-driven methodology to provide a
multi-step prediction of ship roll motions in high sea states. A hybrid neural
network is proposed that combines long short-term memory (LSTM) and
convolutional neural network (CNN) in parallel. The motivation is to extract
the nonlinear dynamic characteristics and the hydrodynamic memory information
through the advantage of CNN and LSTM, respectively. For the feature selection,
the time histories of motion states and wave heights are selected to involve
sufficient information. Taken a scaled KCS as the study object, the ship
motions in sea state 7 irregular long-crested waves are simulated and used for
the validation. The results show that at least one period of roll motion can be
accurately predicted. Compared with the single LSTM and CNN methods, the
proposed method has better performance in predicting the amplitude of roll
angles. Besides, the comparison results also demonstrate that selecting motion
states and wave heights as feature space improves the prediction accuracy,
verifying the effectiveness of the proposed method
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