Luis Itza Vazquez-Salazar,Silvan Käser,Markus Meuwly
Luis Itza Vazquez-Salazar
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods-Ensembles, deep evidential regression (DER), and Gaussian Mixture Model...
Effect of Hubbard U-corrections on the electronic and magnetic properties of 2D materials: a high-throughput study [0.03%]
Hubbard U校正对二维材料电子和磁性性质的影响：一种高通量研究方法
Sahar Pakdel,Thomas Olsen,Kristian S Thygesen
Sahar Pakdel
We conduct a systematic investigation of the role of Hubbard U corrections in electronic structure calculations of two-dimensional (2D) materials containing 3d transition metals. Specifically, we use density functional theory (DFT) with the...
Machine learning Hubbard parameters with equivariant neural networks [0.03%]
利用等变神经网络进行Hubbard参数的机器学习
Martin Uhrin,Austin Zadoks,Luca Binci et al.
Martin Uhrin et al.
Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction err...
Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions [0.03%]
一种利用四体相互作用的混合Transformer图框架加速材料属性预测
Mohammad Madani,Valentina Lacivita,Yongwoo Shin et al.
Mohammad Madani et al.
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Netwo...
A general framework for active space embedding methods with applications in quantum computing [0.03%]
一种主动空间嵌入方法的通用框架及量子计算中的应用研究
Stefano Battaglia,Max Rossmannek,Vladimir V Rybkin et al.
Stefano Battaglia et al.
We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding approaches based on an orbital space separation of the fragment and environment degrees of freedom. We demonstrate its potential by ...
Predicting electronic screening for fast Koopmans spectral functional calculations [0.03%]
预测电子筛选在快速Koopmans光谱泛函计算中的应用
Yannick Schubert,Sandra Luber,Nicola Marzari et al.
Yannick Schubert et al.
Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the ...
Crosslinking degree variations enable programming and controlling soft fracture via sideways cracking [0.03%]
交联度变化通过横向裂纹实现软材料断裂编程和控制
Miguel Angel Moreno-Mateos,Paul Steinmann
Miguel Angel Moreno-Mateos
Large deformations of soft materials are customarily associated with strong constitutive and geometrical nonlinearities that originate new modes of fracture. Some isotropic materials can develop strong fracture anisotropy, which manifests a...
Exciton fine structure in twisted transition metal dichalcogenide heterostructures [0.03%]
扭烯族金属二硫化物异质结构中的激子精细结构
Sudipta Kundu,Tomer Amit,H R Krishnamurthy et al.
Sudipta Kundu et al.
Moiré superlattices of transition metal dichalcogenide (TMD) heterostructures give rise to rich excitonic phenomena associated with the interlayer twist angle. Theoretical calculations of excitons in such systems are typically based on mod...
Range-separated hybrid functionals for accurate prediction of band gaps of extended systems [0.03%]
用于扩展系统准确预测能带隙的分离型杂化泛函
Jing Yang,Stefano Falletta,Alfredo Pasquarello
Jing Yang
In this work, we systematically evaluate the accuracy in band gap prediction of range-separated hybrid functionals on a large set of semiconducting and insulating materials and carry out comparisons with the performance of their global coun...
Austin Zadoks,Antimo Marrazzo,Nicola Marzari
Austin Zadoks
Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned...