XACS

Xiamen Atomistic Computing Suite

XMVB

XMVB

A Valence Bond Theory Based Quantum Chemistry Program

XMVB

XEDA

XEDA

A General and Multipurpose Energy Decomposition Analysis Program

XEDA

Utils

MLatom

A Package for Atomistic Simulations with Machine Learning

MLatom

PreCal

XSCF

An Electronic Structure Calculation Program

XSCF

XGUI

XGUI

A Graphic Interface for XMVB Calculation and Orbital Visualization

XGUI

AFCG

AFCG

Automatic Generator of Formula and Code Based on Nonorthogonal Orbitals

AFCG
News
2
November
2022

Mach. Learn. Sci. Technol.: A comparative study of different machine learning methods for dissipative quantum dynamics

The comparative study is performed for a general two-state spin-boson model where the performance of the models was assessed by the mean absolute error (MAE) and computational times for training and prediction.

4
October
2022

New book: Quantum Chemistry in the Age of Machine Learning

Prof. Pavlo O. Dral's book “Quantum Chemistry in the Age of Machine Learning” was published by Elsevier on 16th September, 2022.

21
September
2022

Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn]

MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school.

9
September
2022

J. Chem. Phys.: the classic but challenging covalent-ionic interaction in LiF

J. Chem. Phys. 157, 084106 (2022); doi: 10.1063/5.0097614