SPRING, an effective and reliable framework for image reconstruction in single-particle Coherent Diffraction Imaging [0.03%]
一种有效的单粒子相干衍射成像图像重建框架：SPRING
Alessandro Colombo,Mario Sauppe,Andre Al Haddad et al.
Alessandro Colombo et al.
Coherent Diffraction Imaging (CDI) is an experimental technique to image isolated structures by recording the scattered light. The sample density can be recovered from the scattered field through a Fourier Transform operation. However, the ...
Accelerating domain-aware electron microscopy analysis using deep learning models with synthetic data and image-wide confidence scoring [0.03%]
基于合成数据和图像范围自信评分的深度学习模型加速领域感知电子显微镜分析
M J Lynch,R Jacobs,G A Bruno et al.
M J Lynch et al.
The integration of machine learning (ML) models enhances the efficiency, affordability, and reliability of feature detection in microscopy, yet their development and applicability are hindered by the dependency on scarce and often flawed ma...
Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning [0.03%]
基于冻结转移学习的材料相互原子势能基模型的微调
Mariia Radova,Wojciech G Stark,Connor S Allen et al.
Mariia Radova et al.
Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training ...
Jakub Šebesta,Oscar Grånäs
Jakub Šebesta
The use of ultrashort laser pulses to manipulate properties or investigate a materials response on femtosecond time-scales enables detailed tracking of charge, spin, and lattice degrees of freedom. When pushing the limits of experimental re...
Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials [0.03%]
基于数据驱动的Ag-Bi-I钙钛矿型衍生材料微观结构优化
Kshithij Mysore Nandishwara,Shuan Cheng,Pengjun Liu et al.
Kshithij Mysore Nandishwara et al.
Microstructural design is crucial yet challenging for thin-film semiconductors, creating barriers for new materials to achieve practical applications in photovoltaics and optoelectronics. We present the Daisy Visual Intelligence Framework (...
Samuel J R Holt,Martin Lang,James C Loudon et al.
Samuel J R Holt et al.
We have designed and implemented the Python package mag2exp, which enables researchers to perform a range of virtual experiments given a spatially resolved vector field for the magnetization, a typical result from computational methods to s...
Machine learning and data-driven methods in computational surface and interface science [0.03%]
机器学习和数据驱动方法在计算表面和界面科学中的应用
Lukas Hörmann,Wojciech G Stark,Reinhard J Maurer
Lukas Hörmann
Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling...
NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics [0.03%]
神经磁学：一种开放源码节点有限差分逆微磁学代码
C Abert,F Bruckner,A Voronov et al.
C Abert et al.
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on vario...
First-principles Hubbard parameters with automated and reproducible workflows [0.03%]
基于自动化和可重复工作流程的第一性原理Hubbard参数
Lorenzo Bastonero,Cristiano Malica,Eric Macke et al.
Lorenzo Bastonero et al.
We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard U and V parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters i...
Sofia Sheikh,Brent Vela,Pejman Honarmandi et al.
Sofia Sheikh et al.
Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of c...