GEMDAT: a Python toolkit for site-resolved diffusion analysis in solid-state molecular dynamics [0.03%]
GEMDAT：一个用于固态分子动力学位点解析扩散分析的Python工具包
Anastasia K Lavrinenko,Theodosios Famprikis,Victor Landgraf et al.
Anastasia K Lavrinenko et al.
Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials such as ionic conductors, fuel cells, and gas sensors, yet most existing studies and software tools extract only stand...
Nonequilibrium photocarrier and phonon dynamics from first principles: a unified treatment of carrier-carrier, carrier-phonon, and phonon-phonon scattering [0.03%]
从头算非平衡光载流子和声子动力学：载体-载体、载体-声子和声子-声子散射的统一处理方法
Stefano Mocatti,Giovanni Marini,Giulio Volpato et al.
Stefano Mocatti et al.
We develop a first-principles many-body framework to describe photocarrier and phonon dynamics in semiconductors after ultrafast excitation. The method includes explicit ab initio light-matter coupling, collision integrals for carrier-carri...
Automated modeling of polarons: defects and reactivity on TiO2(110) surfaces [0.03%]
基于TiO2（110）表面缺陷和反应性的自动极化子建模
Firat Yalcin,Carla Verdi,Viktor C Birschitzky et al.
Firat Yalcin et al.
Polarons are widespread in functional materials and are key to device performance in several technological applications. However, their effective impact on material behavior remains elusive, as condensed matter studies struggle to capture t...
Exploring charge density waves in two-dimensional NbSe2 with machine learning [0.03%]
基于机器学习的二维NbSe2电荷密度波研究
Norma Rivano,Francesco Libbi,Chuin Wei Tan et al.
Norma Rivano et al.
Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs-capturing effects such as layer numbe...
Morphology prediction of small nanoparticles in any orientation from single electron micrographs [0.03%]
基于单电子显微图像预测任意取向的小纳米颗粒的形貌
Henrik Eliasson,Fangjinhua Wang,Xi Wang et al.
Henrik Eliasson et al.
Accurate and automated data analysis for transmission electron microscopy will enable new high-throughput experiments that can reveal atomic-scale structure-property relationships for many functional materials. A key challenge in this pursu...
Cheuk Hin Ho,Christoph Ortner,YangShuai Wang
Cheuk Hin Ho
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction interva...
Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges [0.03%]
视觉语言模型的科学图像分析评测：机遇与挑战并存
Prateek Verma,Minh-Hao Van,Xintao Wu
Prateek Verma
Recent advancements in vision language models (VLMs) have opened new avenues for analyzing complex visual data. Models such as ChatGPT, Gemini, Llama and LLaVA have gained prominence for their ability to process both visual and textual data...
Chongxiao Fan,Emil Viñas Boström,Xinle Cheng et al.
Chongxiao Fan et al.
Controlling materials through their interactions with electromagnetic vacuum fluctuations is an emergent frontier in material engineering. Although recent experiments have demonstrated dark cavity effects for electronic material phases, lik...
Extraction of the self energy and Eliashberg function from angle resolved photoemission spectroscopy using the xARPES code [0.03%]
利用x ARPES代码从角度解析光电子能谱中提取自能和Eliashberg函数
Thomas P van Waas,Christophe Berthod,Jan Berges et al.
Thomas P van Waas et al.
Angle-resolved photoemission spectroscopy is a powerful experimental technique for studying anisotropic many-body interactions through the electron spectral function. Existing attempts to decompose the spectral function into non-interacting...
Equivariant electronic Hamiltonian prediction with many-body message passing [0.03%]
基于多体消息传递的等变电子哈密顿量预测
Chen Qian,Valdas Vitartas,James R Kermode et al.
Chen Qian et al.
Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. F...