arXiv subjects

Jian Lin

Publications and source records attributed to Jian Lin.

32 records · Page 2Linked to original sources

arXiv2023

Scientific Machine Learning Framework to Understand Flash Graphene Synthesis

Flash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promise in scalability and performance, attempts to explore the reaction mechanism have been limited due to complexity involved in the FFE process. Data-driven machine learning (ML) models effectively account for this complexity, but the model training requires considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 +/- 0.05 and an average RMSE of 12.1% +/- 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 +/- 0.05 and an RMSE of 14.3% +/- 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes.

physics.app-ph↗

arXiv2022

Artificial Intelligence Security Competition (AISC)

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

cs.CR↗

arXiv2021

Hard instance learning for quantum adiabatic prime factorization

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and un-configured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the un-configured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable -- the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost.

quant-ph↗

arXiv2021

Quantum Adiabatic Doping for Atomic Fermi-Hubbard Quantum Simulations

There have been considerable research efforts devoted to quantum simulations of Fermi-Hubbard model with ultracold atoms loaded in optical lattices. In such experiments, the antiferromagnetically ordered quantum state has been achieved at half filling in recent years. The atomic lattice away from half filling is expected to host d-wave superconductivity, but its low temperature phases have not been reached. In a recent work, we proposed an approach of incommensurate quantum adiabatic doping, using quantum adiabatic evolution of an incommensurate lattice for preparation of the highly correlated many-body ground state of the doped Fermi-Hubbard model starting from a unit-filling band insulator. Its feasibility has been demonstrated with numerical simulations of the adiabatic preparation for certain incommensurate particle-doping fractions, where the major problem to circumvent is the atomic localization in the incommensurate lattice. Here we carry out a systematic study of the quantum adiabatic doping for a wide range of doping fractions from particle-doping to hole-doping, including both commensurate and incommensurate cases. We find that there is still a localization-like slowing-down problem at commensurate fillings, and that it becomes less harmful in the hole-doped regime. With interactions, the adiabatic preparation is found to be more efficient for that interaction effect destabilizes localization. For both free and interacting cases, we find the adiabatic doping has better performance in the hole-doped regime than the particle-doped regime. We also study adiabatic doping starting from the half-filling Mott insulator, which is found to be more efficient for certain filling fractions.

cond-mat.quant-gas↗

arXiv2020

Shelf-stable quantum-dot light-emitting diodes with high operational performance

Quantum-dot light-emitting diodes (QLEDs) promise high-performance and cost-effective electroluminescent devices. However, shelf stability of QLEDs, a must for practical applications, is currently overlooked. Here we reveal that the state-of-the-art QLEDs exhibit abnormal shelf-ageing behaviours, i.e., improvements in performance (positive ageing), followed by deterioration of performance (negative ageing). Mechanism studies show that the organic acids in the encapsulation acrylic resin induce in-situ reactions, which simultaneously improve electrical conductance and minimize interfacial exciton quenching at the positive-ageing stage. Progression of the in-situ reactions results in negative ageing. Inspired by these findings, we design an electron-transporting bi-layer structure, which delivers both improved electrical conductivity and suppressed interfacial exciton quenching. This design enables shelf-stable QLEDs with high operational performance, i.e., neglectable changes of external quantum efficiency (>20.0%) and ultra-long operational lifetime (T95: 5,500 hours at 1,000 cd m-2) after storage for 180 days. Our work paves the way towards the commercialization of QLEDs.

physics.app-ph↗

arXiv2020

Quantum Adiabatic Algorithm Design using Reinforcement Learning

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL-design automatically produces an adiabatic quantum algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-designed algorithm has a better performance compared to the analytically constructed non-linear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-design approach remains applicable even when the non-linear Hamiltonian path is not analytically available. In 3-SAT, we find RL-design has fascinating transferability---the adiabatic algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic algorithm design. Further considering the established complexity-equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach is potentially applicable to different quantum hardwares from trapped-ions and optical-lattices to superconducting-qubit devices.

quant-ph↗

arXiv2019

Rapid Identification of X-ray Diffraction Spectra Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs. Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR.

physics.data-an↗

arXiv2019

Quantum Adiabatic Doping with Incommensurate Optical Lattices

Quantum simulations of Fermi-Hubbard models have been attracting considerable efforts in the optical lattice research, with the ultracold anti-ferromagnetic atomic phase reached at half filling in recent years. An unresolved issue is to dope the system while maintaining the low thermal entropy. Here we propose to achieve the low temperature phase of the doped Fermi-Hubbard model using incommensurate optical lattices through adiabatic quantum evolution. In this theoretical proposal, we find that one major problem about the adiabatic doping that shows up is atomic localization in the incommensurate lattice, potentially causing exponential slowing down of the adiabatic procedure. We study both one- and two-dimensional incommensurate optical lattices, and find that the localization prevents efficient adiabatic doping in the strong lattice regime for both cases. With density matrix renormalization group calculation, we further show that the slowing down problem in one dimension can be circumvented by considering interaction induced many-body delocalization, which is experimentally feasible using Feshbach resonance techniques. This protocol is expected to be efficient as well in two dimensions where the localization phenomenon is less stable.

cond-mat.quant-gas↗

arXiv2019

Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN

Inverse design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. The massive combinational spaces due to the constituent elements and their structural configurations are too overwhelming to be all searched even by high-throughput computations. Herein, we demonstrated a novel regressional generative adversarial network (RGAN) for inverse design of representative two-dimensional materials, graphene and boron-nitride (BN) hybrids. A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels. The proposed RGAN enables to autonomously generate graphene/BN hybrids with any given bandgaps. Moreover, the generated structures exhibit high fidelity, yielding bandgaps within ~ 10% MAEF of the desired bandgaps as cross-validated by density functional theory (DFT) calculations. Further analysis by principle component analysis (PCA) and modified locally linear embedding (MLLE) methods on the latent features encoded by the regressor reveals that the generator has successfully generated structures that followed the statistical distribution of the real structures. It implies the possibility of the RGAN in recognizing physical rules hidden in the high-dimensional data. This new inverse design methodology would speed up the discovery and development of other 2D materials and beyond.

physics.comp-ph↗

arXiv2018

Deep Learning Bandgaps of Topologically Doped Graphene

Manipulation of material properties via precise doping affords enormous tunable phenomena to explore. Recent advance shows that in the atomic and nano scales topological states of dopants play crucial roles in determining their properties. However, such determination is largely unknown due to the incredible size of topological states. Here, we present a case study of developing deep learning algorithms to predict bandgaps of boron-nitrogen pair doped graphene with arbitrary dopant topologies. A material descriptor system that enables to correlate structures with the bandgaps was developed for convolutional neuron networks (CNNs). Bandgaps calculated by the ab initio calculations and the corresponding structures were fed as input datasets to train VGG16 convolutional network, residual convolutional network, and concatenate convolutional network. Then these trained CNNs were used to predict bandgaps of doped graphene with various dopant topologies. All of them afford great prediction accuracy, showing square of the coefficient of correlation (R2) of > 90% and root-mean-square errors of ~ 0.1 eV for the predicted bandgaps. They are much better than those predicted by a shallow machine learning method - support vector machine. The transfer learning was further performed by leveraging data generated from smaller systems to improve the prediction for large systems. Success of this work provides a cornerstone for future investigation of topologically doped graphene and other 2D materials. Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales.

cond-mat.mtrl-sci↗

arXiv2017

SpecWatch: A Framework for Adversarial Spectrum Monitoring with Unknown Statistics

In cognitive radio networks (CRNs), dynamic spectrum access has been proposed to improve the spectrum utilization, but it also generates spectrum misuse problems. One common solution to these problems is to deploy monitors to detect misbehaviors on certain channel. However, in multi-channel CRNs, it is very costly to deploy monitors on every channel. With a limited number of monitors, we have to decide which channels to monitor. In addition, we need to determine how long to monitor each channel and in which order to monitor, because switching channels incurs costs. Moreover, the information about the misuse behavior is not available a priori. To answer those questions, we model the spectrum monitoring problem as an adversarial multi-armed bandit problem with switching costs (MAB-SC), propose an effective framework, and design two online algorithms, SpecWatch-II and SpecWatch-III, based on the same framework. To evaluate the algorithms, we use weak regret, i.e., the performance difference between the solution of our algorithm and optimal (fixed) solution in hindsight, as the metric. We prove that the expected weak regret of SpecWatch-II is O(T^{2/3}), where T is the time horizon. Whereas, the actual weak regret of SpecWatch-III is O(T^{2/3}) with probability 1 - δ, for any δ in (0, 1). Both algorithms guarantee the upper bounds matching the lower bound of the general adversarial MAB- SC problem. Therefore, they are all asymptotically optimal.

cs.IT↗

arXiv2014

Hydrogen Diffusion and Stabilization in Single-crystal VO2 Micro/nanobeams by Direct Atomic Hydrogenation

We report measurements of the diffusion of atomic hydrogen in single crystalline VO2 micro/nanobeams by direct exposure to atomic hydrogen, without catalyst. The atomic hydrogen is generated by a hot filament, and the doping process takes place at moderate temperature (373 K). Undoped VO2 has a metal-to-insulator phase transition at ~340 K between a high-temperature, rutile, metallic phase and a low-temperature, monoclinic, insulating phase with a resistance exhibiting a semiconductor-like temperature dependence. Atomic hydrogenation results in stabilization of the metallic phase of VO2 micro/nanobeams down to 2 K, the lowest point we could reach in our measurement setup. Based on observing the movement of the hydrogen diffusion front in single crystalline VO2 beams, we estimate the diffusion constant for hydrogen along the c-axis of the rutile phase to be 6.7 x 10^{-10} cm^2/s at approximately 373 K, exceeding the value in isostructural TiO2 by ~ 38x. Moreover, we find that the diffusion constant along the c-axis of the rutile phase exceeds that along the equivalent a-axis of the monoclinic phase by at least three orders of magnitude. This remarkable change in kinetics must originate from the distortion of the "channels" when the unit cell doubles along this direction upon cooling into the monoclinic structure. Ab initio calculation results are in good agreement with the experimental trends in the relative kinetics of the two phases. This raises the possibility of a switchable membrane for hydrogen transport.

cond-mat.mtrl-sci↗

arXiv2013

CORE: Augmenting Regenerating-Coding-Based Recovery for Single and Concurrent Failures in Distributed Storage Systems

Data availability is critical in distributed storage systems, especially when node failures are prevalent in real life. A key requirement is to minimize the amount of data transferred among nodes when recovering the lost or unavailable data of failed nodes. This paper explores recovery solutions based on regenerating codes, which are shown to provide fault-tolerant storage and minimum recovery bandwidth. Existing optimal regenerating codes are designed for single node failures. We build a system called CORE, which augments existing optimal regenerating codes to support a general number of failures including single and concurrent failures. We theoretically show that CORE achieves the minimum possible recovery bandwidth for most cases. We implement CORE and evaluate our prototype atop a Hadoop HDFS cluster testbed with up to 20 storage nodes. We demonstrate that our CORE prototype conforms to our theoretical findings and achieves recovery bandwidth saving when compared to the conventional recovery approach based on erasure codes.

cs.DC↗

arXiv2009

Write-once read-many-times memory based on a single layer of Pentacene

We realized an organic electrical memory device with a simple structure based on single layer pentacene film embedded between Al and ITO electrodes. The optimization of the thickness and deposition rate of pentacene resulted in a reliable device with an ON/OFF current ratio as high as nearly 1000000 which was two orders of magnitude higher than previous results, and the storage time was more than 576 hrs. The current transition process is attributed to the formation and damage of the interface dipole at different electric fields, in which the current conduction showed a transition from Ohmic conductive current to Fowler?Nordheim tunnelling current. After the transition from ON state to OFF state, the device tended to remain in OFF state even when the applied voltage was removed, which indicated that the device was very promising for write-once read-many-times (WORM) memory.

cond-mat.mtrl-sci↗