STEM Diffraction Pattern Analysis with Deep Learning Networks

Authors: Sebastian Wissel, Jonas Scheunert, Aaron Dextre, Shamail Ahmed, Andreas Bayer, Kerstin Volz, Bai-Xiang Xu

Published: July 2, 2025

Accurate grain orientation mapping is essential for understanding and
optimizing the performance of polycrystalline materials, particularly in
energy-related applications. Lithium nickel oxide (LiNiO$_{2}$) is a promising
cathode material for next-generation lithium-ion batteries, and its
electrochemical behaviour is closely linked to microstructural features such as
grain size and crystallographic orientations. Traditional orientation mapping
methods--such as manual indexing, template matching (TM), or Hough
transform-based techniques--are often slow and noise-sensitive when handling
complex or overlapping patterns, creating a bottleneck in large-scale
microstructural analysis. This work presents a machine learning-based approach
for predicting Euler angles directly from scanning transmission electron
microscopy (STEM) diffraction patterns (DPs). This enables the automated
generation of high-resolution crystal orientation maps, facilitating the
analysis of internal microstructures at the nanoscale. Three deep learning
architectures--convolutional neural networks (CNNs), Dense Convolutional
Networks (DenseNets), and Shifted Windows (Swin) Transformers--are evaluated,
using an experimentally acquired dataset labelled via a commercial TM
algorithm. While the CNN model serves as a baseline, both DenseNets and Swin
Transformers demonstrate superior performance, with the Swin Transformer
achieving the highest evaluation scores and the most consistent microstructural
predictions. The resulting crystal maps exhibit clear grain boundary
delineation and coherent intra-grain orientation distributions, underscoring
the potential of attention-based architectures for analyzing diffraction-based
image data. These findings highlight the promise of combining advanced machine
learning models with STEM data for robust, high-throughput microstructural
characterization.

An Adaptive Estimation Approach based on Fisher Information to Overcome the Challenges of LFP Battery SOC Estimation

Authors: Junzhe Shi, Shida Jiang, Shengyu Tao, Jaewong Lee, Manashita Borah, Scott Moura

Published: July 1, 2025

Robust and Real-time State of Charge (SOC) estimation is essential for
Lithium Iron Phosphate (LFP) batteries, which are widely used in electric
vehicles (EVs) and energy storage systems due to safety and longevity. However,
the flat Open Circuit Voltage (OCV)-SOC curve makes this task particularly
challenging. This challenge is complicated by hysteresis effects, and
real-world conditions such as current bias, voltage quantization errors, and
temperature that must be considered in the battery management system use. In
this paper, we proposed an adaptive estimation approach to overcome the
challenges of LFPSOC estimation. Specifically, the method uses an adaptive
fisher information fusion strategy that adaptively combines the SOC estimation
from two different models, which are Coulomb counting and equivalent circuit
model-based parameter identification. The effectiveness of this strategy is
rationalized by the information richness excited by external cycling signals. A
3D OCV-H-SOC map that captures the relationship between OCV, hysteresis, and
SOC was proposed as the backbone, and can be generalizable to other widely
adopted parameter-identification methods. Extensive validation under ideal and
real-world use scenarios, including SOC-OCV flat zones, current bias, voltage
quantization errors, low temperatures, and insufficient current excitations,
have been performed using 4 driving profiles, i.e., the Orange County Transit
Bus Cycle, the California Unified Cycle, the US06 Drive Cycle, and the New York
City Cycle, where the results demonstrate superiority over the state-of-the-art
unscented Kalman filter, long short-term memory networks and transformer in all
validation cases.

Photospheric Helium Abundance in Cool Giants -- A Comprehensive Study

Authors: B. P. Hema, Gajendra Pandey

Published: July 1, 2025

High-resolution optical spectra of sixteen red giants, two early asymptotic
giant branch (AGB) stars, and two supergiants, having no/minimal to super
lithium (Li)-rich abundances, are analyzed to investigate the helium
(He)-enhancement. The spectra of eight giants were obtained from the Himalayan
Chandra Telescope, and for the rest of the program stars the spectral data were
collected from various public archives. Our detailed abundance analyses of the
program stars involve the determination of stellar parameters and abundances
for about 20 elements among the key abundances of He, Li, C, N, O and the
12C/13C ratios. The difference in the Mg-abundance derived from Mg I lines and
the MgH band, and the difference in carbon abundance from C I and the CH-band,
are used as a clue to the mild hydrogen-deficiency/helium-enhancement. From
this analysis, four red giants, an early-AGB star and a supergiant star were
found to be enhanced in helium. All these He-enhanced stars are also found to
be super Li-rich except for the supergiant. Since the He-rich red giants are
Li-rich as well, this implies, that He-enrichment is accompanied by
Li-enrichment but not vice-versa. This is the first spectroscopic measurement
of photospheric He-abundance in normal and Li-rich field giants. The
Li-enrichment is observed across the giant branch from RGB-bump (KIC 9821622)
to AGB phase, unlike that expected from the RGB-tip to RC-phase. A plausible
scenario for the enrichment of He and as well as Li in giants is the fresh
synthesis of Li in the interiors of giants and dredging up along with He to the
surface, from deeper layers. However, there could be multiple scenarios
operating in tandem. This analysis of He- and Li-enrichment along with other
key elements provides more insights to decipher the mystery of Li-enrichment in
giants.

Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction

Authors: Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova

Published: July 1, 2025

Accurate electrochemical models are essential for the safe and efficient
operation of lithium-ion batteries in real-world applications such as
electrified vehicles and grid storage. Reduced-order models (ROM) offer a
balance between fidelity and computational efficiency but often struggle to
capture complex and nonlinear behaviors, such as the dynamics in the cell
voltage response under high C-rate conditions. To address these limitations,
this study proposes an Adaptive Ensemble Sparse Identification (AESI) framework
that enhances the accuracy of reduced-order li-ion battery models by
compensating for unpredictable dynamics. The approach integrates an Extended
Single Particle Model (ESPM) with an evolutionary ensemble sparse learning
strategy to construct a robust hybrid model. In addition, the AESI framework
incorporates a conformal prediction method to provide theoretically guaranteed
uncertainty quantification for voltage error dynamics, thereby improving the
reliability of the model's predictions. Evaluation across diverse operating
conditions shows that the hybrid model (ESPM + AESI) improves the voltage
prediction accuracy, achieving mean squared error reductions of up to 46% on
unseen data. Prediction reliability is further supported by conformal
prediction, yielding statistically valid prediction intervals with coverage
ratios of 96.85% and 97.41% for the ensemble models based on bagging and
stability selection, respectively.

Superconducting gap and its Little-Parks like oscillations with high-order harmonics in lithium intercalated 1T-TiSe$_2$

Authors: Jia-Yi Ji, Zongzheng Cao, Yi Hu, Haoyang Wu, Heng Wang, Yuying Zhu, Haiwen Liu, Lexian Yang, Qi-Kun Xue, Ding Zhang

Published: June 30, 2025

The superconducting phase of doped 1T-TiSe$_2$ is a fruitful playground for
exploring exotic quantum phenomena such as the anomalous metal state and
spontaneously formed superconducting network. Here, we address these emergent
states by studying the superconducting gap of lithium intercalated TiSe$_2$-a
fundamental quantity that has remained unexplored so far. We fabricate a device
that combines solid-state lateral lithium intercalation, resistance
measurements and tunneling spectroscopy. We successfully probe the
superconducting gap of TiSe$_2$ and reveal that the gap closing temperature
well exceeds the transition temperature ($T_c$) expected from the
Bardeen-Cooper-Schrieffer theory, indicating pronounced superconducting
fluctuations even in a bulk-like system. Moreover, the symmetric gap persists
even in the anomalous metal state, demonstrating the particle-hole symmetry of
this exotic phase directly from the density of states. Finally, the
superconducting gap shows magneto-oscillations with higher harmonics, attesting
to a rather regular structure of the intrinsic superconducting network.

Bistable quartic soliton in saturable nonlinear media

Authors: Tiyas Das, Anuj Pratim Lara, Samudra Roy

Published: June 30, 2025

In this letter, for the first time, to the best of our knowledge, we
theoretically demonstrate the existence of novel bistable quartic soliton (BQS)
in saturable nonlinear media. We propose a realistic dispersion-engineered
ridge waveguide based
  on Lithium Niobate (LiNbO3) that offers a suitable environment to excite the
family of BQS. Adopting the variational method we analytically establish the
amplitude-width relation which reveals that stable QSs with identical duration
but two different amplitudes can coexist. We further investigate the robustness
of such BQS under perturbation by performing the linear stability analysis.

Integrated bright source of polarization-entangled photons using lithium niobate photonic chips

Authors: Changhyun Kim, Hansol Kim, Minho Choi, Junhyung Lee, Yongchan Park, Sunghyun Moon, Jinil Lee, Hyeon Hwang, Min-Kyo Seo, Yoon-Ho Kim, Yong-Su Kim, Hojoong Jung, Hyounghan Kwon

Published: June 30, 2025

Quantum photonics has rapidly advanced as a key area for developing quantum
technologies by harnessing photons' inherent quantum characteristics,
particularly entanglement. Generation of entangled photon pairs, known as Bell
states, is crucial for quantum communications, precision sensing, and quantum
computing. While bulk quantum optical setups have provided foundational
progress, integrated quantum photonic platforms now offer superior scalability,
efficiency, and integrative potential. In this study, we demonstrate a compact
and bright source of polarization-entangled Bell state utilizing
continuous-wave pumping on thin film lithium niobate (TFLN) integrated
photonics. Our periodically poled lithium niobate device achieves on-chip
brightness of photon pair generation rate of 508.5 MHz/mW, surpassing other
integrated platforms including silicon photonics. This demonstration marks the
first realization of polarization entanglement on TFLN platforms.
Experimentally measured metrics confirm high-quality entangled photon pairs
with a purity of 0.901, a concurrence of 0.9, and a fidelity of 0.944. We
expect our compact quantum devices to have great potential for advancing
quantum communication systems and photonic quantum technologies.

50 GHz Piezoelectric Acoustic Filter

Authors: Omar Barrera, Jack Kramer, Lezli Matto, Vakhtang Chulukhadze, Sinwoo Cho, Michael Liao, Mark S. Goorsky, Ruochen Lu

Published: June 27, 2025

This paper presents significant frequency scaling of acoustic filter
technology to 50 GHz. This achievement is enabled by the P3F LiNbO3 multilayer
stack, in which piezoelectric thin-films of alternating orientations are
transferred in sequence, thereby allowing efficient exploitation of high-order
modes with high quality factor (Q) and coupling coefficient (k2) in a thicker
piezoelectric stack. The demonstrated filter is comprised of twelfth-order
symmetric (S12) mode lateral-field-excited bulk acoustic wave resonators
(XBARs), built on a 4-layer periodically poled piezoelectric (P3F) 128 Y-cut
lithium niobate (LiNbO3) stack. The filter exhibits 3.3 dB insertion loss (IL)
and a fractional bandwidth (FBW) of 2.9%. The miniature design, with a
footprint of 0.36 mm2, makes it promising for future wireless front-end
applications. These results represent the highest frequency acoustic filters
reported to date, setting a new benchmark in piezoelectric filter technology.
Upon further development, the platform could enable filters further into the
FR2 range, essential for next-generation communication systems.

19.3 GHz Acoustic Filter with High Close-in Rejection in Tri-layer Thin-Film Lithium Niobate

Authors: Omar Barrera, Sinwoo Cho, Jack Kramer, Vakhtang Chulukhadze, Tzu-Hsuan Hsu, Ruochen Lu

Published: June 27, 2025

Acoustic filters are preferred front-end solutions at sub-6 GHz due to their
superior frequency selectivity compared to electromagnetic (EM) counterparts.
With the ongoing development of 5G and the evolution toward 6G, there is a
growing need to extend acoustic filter technologies into frequency range 3
(FR3), which spans 7 to 24 GHz to accommodate emerging high-frequency bands.
However, scaling acoustic filters beyond 10 GHz presents significant
challenges, as conventional platforms suffer from increased insertion loss (IL)
and degraded out-of-band (OoB) rejection at higher frequencies. Recent
innovations have led to the emergence of periodically poled piezoelectric
lithium niobate (P3F LN) laterally excited bulk acoustic resonators (XBARs),
offering low-loss and high electromechanical coupling performance above 10 GHz.
This work presents the first tri-layer P3F LN filter operating at 19.3 GHz,
achieving a low IL of 2.2 dB, a 3-dB fractional bandwidth (FBW) of 8.5%, and an
impressive 49 dB close in rejection. These results demonstrate strong potential
for integration into FR3 diplexers.

Narrow beam and low-sidelobe two-dimensional beam steering on thin-film lithium niobate optical phased array

Authors: Yang Li, Shiyao Deng, Xiao Ma, Ziliang Fang, Shufeng Li, Weikang Xu, Fangheng Fu, Xu Ouyang, Yuming Wei, Tiefeng Yang, Heyuan Guan, Huihui Lu

Published: June 27, 2025

Optical beam steering has become indispensable in free-space optical
communications, light detection and ranging (LiDAR), mapping, and projection.
Optical phased array (OPA) leads this field, yet conventional versions still
suffer from a narrow steering field of view (FOV), insufficient sidelobe
suppression, and limited angular resolution. Thin-film lithium niobate (LN),
with its strong Pockels electro-optic (EO) effect, offers a powerful
integrated-photonics platform to overcome these limitations. Here we present a
two-dimensional (2D) EO-steered OPA based on a non-uniformly spaced X-cut
thin-film LN ridge-waveguide array. A superlattice ridge design suppresses
optical crosstalk to -20 dB, enabling low-sidelobe far-field radiation. Using
particle swarm optimization (PSO) method, we transform a uniformly spaced array
into an optimized non-uniform design, largely improving angular resolution
while maintaining sidelobe suppression. When combined with a single-radiating
trapezoidal end-fire emitter incorporating an etched grating, the device
produces a main-lobe beam width of 0.99 degree*0.63 degree from an aperture of
only 140 um*250 um, achieving a wide 2D steering range of 47 degree*9.36 degree
with a 20 dB sidelobe-suppression ratio. These results highlight thin-film LN
OPA as a compelling route toward heterogeneous, compact, and high-performance
EO beam-steering modules and ultra-miniaturized optical modulators.