XMVB
Chem. Eur. J.: Application of Valence Bond Theory to Captodative Effect in Diboron Molecule (CAAC)2B2(SH)2
The study is valuable in guiding the design and performance regulation of diboron compounds.
JACS: Application of valence bond theory to planar quaternary bis-boron actinides with Double Möbius Aromaticity
This research highlight showcases the application of block-localized wavefunction method with the XMVB@XACS software package by Xuhui Lin et al. at Central South University.
J. Chem. Theory Comput.: Hybrid Density Functional Valence Bond Method with Multistate Treatment
Chemical bonding group from Xiamen University has developed a hybrid density functional valence bond (VB) method with multistate treatment, namely λ-DFVB(MS).
XEDA
JPCA: QM/MM-Based Energy Decomposition Analysis Method for Large Systems
It plays a significant role in enhancing our understanding of various chemical phenomena.
JPCA：基于QM/MM的大分子体系能量分解分析方法
最近，苏培峰课题组将QM/MM方法与GKS-EDA相结合，提出了GKS-EDA(QM/MM)，用于大分子体系的相互作用，工作发表在J. Phys. Chem. A。
Inorg. Chem. : Application of chemical bond analysis in lithium-sulfur batteries design
New example on the application of XACS showcases the study of Wang Changwei from Shaanxi Normal University and Mo Yirong from the University of North Carolina at Greensboro.
MLatom
Chem. Commun. Feature Article: AI in computational chemistry through the lens of a decade-long journey
It gives a perspective on the progress of AI tools in computational chemistry through the lens of his decade-long contributions put in the wider context of the trends in this rapidly expanding field.
Nat. Commun.: Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by AIQM1
Recently, the Chung group at Southern University of Science and Technology (SUSTech) has combined efficient machine learning potentials (MLPs) with multi-scale quantum refinement methods to enhance computational efficiency and reliability.
MLatom@XACS for AI-enhanced computational chemistry: JCTC paper and online tutorial
MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.