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Physics#Superconductivity, Condensed Matter Physics🔬 ResearchAnalyzed: Jan 3, 2026 17:09

Unconventional Superconductivity in MoTe2 Studied with Kinetic Inductance

Published:Dec 31, 2025 06:53
1 min read
ArXiv

Analysis

This paper investigates the pairing symmetry of the unconventional superconductor MoTe2, a Weyl semimetal, using a novel technique based on microwave resonators to measure kinetic inductance. This approach offers higher precision than traditional methods for determining the London penetration depth, allowing for the observation of power-law temperature dependence and the anomalous nonlinear Meissner effect, both indicative of nodal superconductivity. The study addresses conflicting results from previous measurements and provides strong evidence for the presence of nodal points in the superconducting gap.
Reference

“The high precision of this technique allows us to observe power-law temperature dependence of $λ$, and to measure the anomalous nonlinear Meissner effect -- the current dependence of $λ$ arising from nodal quasiparticles. Together, these measurements provide smoking gun signatures of nodal superconductivity.”

PermalinkArXiv
Research#Computational Mechanics📝 BlogAnalyzed: Dec 28, 2025 21:58

Neural Networks for Predicting Structural Displacements on Meshes and Uncertainty-Based Refinement: Architecture Considerations

Published:Dec 27, 2025 23:16
1 min read
r/deeplearning

Analysis

This post from r/deeplearning describes a supervised learning problem in computational mechanics focused on predicting nodal displacements in beam structures using neural networks. The core challenge lies in handling mesh-based data with varying node counts and spatial dependencies. The author is exploring different neural network architectures, including MLPs, CNNs, and Transformers, to map input parameters (node coordinates, material properties, boundary conditions, and loading parameters) to displacement fields. A key aspect of the project is the use of uncertainty estimates from the trained model to guide adaptive mesh refinement, aiming to improve accuracy in complex regions. The post highlights the practical application of deep learning in physics-based simulations.
Reference

“The input is a bit unusual - it's not a fixed-size image or sequence. Each sample has 105 nodes with 8 features per node (coordinates, material properties, derived physical quantities), and I need to predict 105 displacement values.”

Permalinkr/deeplearning
Research#Simulation🔬 ResearchAnalyzed: Jan 10, 2026 08:53

Accelerated Binodal Calculation: Fixed-Volume Gibbs-Ensemble Monte Carlo Shows Promise

Published:Dec 21, 2025 22:08
1 min read
ArXiv

Analysis

This ArXiv article presents a novel approach to accelerate binodal calculations, a computationally intensive process in materials science and chemical engineering. The research focuses on modifying the Gibbs-Ensemble Monte Carlo method, achieving a significant speedup in simulations.
Reference

“A Fixed-Volume Variant of Gibbs-Ensemble Monte Carlo yields Significant Speedup in Binodal Calculation.”

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Research#Solar Cells🔬 ResearchAnalyzed: Jan 10, 2026 10:06

Unveiling Phase Separation Dynamics in Organic Solar Cell Films

Published:Dec 18, 2025 10:35
1 min read
ArXiv

Analysis

This research delves into the fundamental processes affecting the performance of organic solar cells by investigating phase separation during annealing. The study's focus on crystallization and spinodal decomposition provides valuable insights for optimizing device fabrication.
Reference

“The research focuses on the interplay of crystallization and amorphous spinodal decomposition.”

PermalinkArXiv