Tootfinder

First High-Throughput Evaluation of Dark Matter Detector Materials
Sin\'ead M. Griffin, Yonit Hochberg, Benjamin V. Lehmann, Rotem Ovadia, Kristin A. Persson, Bethany A. Suter, Ruo Xi Yang, Wayne Zhao
https://arxiv.org/abs/2506.19905

First High-Throughput Evaluation of Dark Matter Detector Materials
We perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to one thousand materials from the Materials Project database, we project the sensitivity in dark matter parameter space for experiments constructed from each material, including both absorption and scattering processes between dark matter and electrons. Using the anisotropic materials in the dataset, we further compute the level of daily …

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
Maxim Moraru, Weiyi Xia, Zhuo Ye, Feng Zhang, Yongxin Yao, Ying Wai Li, Cai-Zhuang Wang
https://arxiv.org/abs/2506.21449

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from executio…

IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Kibum Kim, Chanyoung Park
https://arxiv.org/abs/2508.16112

IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an important role in laboratory settings due to its high accessibility and low cost. However, existing approaches often fail to reflect expert analytical processes and lack flexibility in incorporating diverse types of chemical knowledge, which is essential in real-world analytical scenarios. In this paper, we propose IR-Agent, a novel multi-agent framewo…

Crosslisted article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[2/2]:
- Asymmetric stress engineering of dense dislocations in brittle superconductors for strong vortex ...
Meng Han, et al.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Songsoo Kim, Seungtae Lee, See Young Lee, Joonho Kim, Keechan Kan, Dukyong Yoon
https://arxiv.org/abs/2506.20112

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Background: The positive predictive value (PPV) of large language model (LLM)-based proofreading for radiology reports is limited due to the low error prevalence. Purpose: To assess whether a three-pass LLM framework enhances PPV and reduces operational costs compared with baseline approaches. Materials and Methods: A retrospective analysis was performed on 1,000 consecutive radiology reports (250 each: radiography, ultrasonography, CT, MRI) from the MIMIC-III database. Two external datasets (C…

Physics-Informed Kolmogorov-Arnold Networks for multi-material elasticity problems in electronic packaging
Yanpeng Gong, Yida He, Yue Mei, Xiaoying Zhuang, Fei Qin, Timon Rabczuk
https://arxiv.org/abs/2508.16999

Physics-Informed Kolmogorov-Arnold Networks for multi-material elasticity problems in electronic packaging
This paper proposes a Physics-Informed Kolmogorov-Arnold Network (PIKAN) method for analyzing elasticity problems in electronic packaging multi-material structures. The core innovation lies in replacing Multi-Layer Perceptrons (MLPs) with Kolmogorov-Arnold Networks (KANs) within the energy-based Physics-Informed Neural Networks (PINNs) framework. The method constructs admissible displacement fields that automatically satisfy essential boundary conditions and employs various numerical integratio…

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
Harshan Reddy Gopidi, Ruiqi Zhang, Yanyong Wang, Abhirup Patra, Jianwei Sun, Adrienn Ruzsinszky, John P. Perdew, Pieremanuele Canepa
https://arxiv.org/abs/2506.20635 …

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density
DFT is vital for materials discovery, and at the base of extensive molecular and materials databases, chemical reaction predictions, and machine learning potentials. The widespread use of DFT in chemistry and materials science aims for "chemical accuracy," but this is limited by the unknown exchange and correlation (XC) functional. A meta-GGA, the restored regularized strongly constrained and appropriately normed, r r2SCAN XC functional, fulfils 17 exact constraints of the XC energy. r2SCAN sti…

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Energy Window Augmented Plane Waves Approach to Density Functional Theory
Garry Goldstein

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Standard model of electromagnetism and chirality in crystals
R. Winkler, U. Z\"ulicke

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Molecular Cross-linking of MXenes: Tunable Interfaces and Chemiresistive Sensing
Yudhajit Bhattacharjee, et al.