Xiamen Valence Bond (XMVB) is a quantum chemistry program based on valence bond (VB) theory. XMVB provides an ab initio computing platform for various VB approaches, including classical VB methods, such as VBSCF, BOVB, VBCI, VBPT2, modern VB methods, such as SCVB and GVB, and molecular orbitals based VB method, BLW. Combined with solvation models, it can perform VBPCM, VBEFP, and VBSMD to account for solvent effects. Incorporating XMVB with KS-DFT code, it can be applied to hybrid DFVB calculation. It has 237 users in 45 countries around the world.
For details, please read the TUTORIALS and MANUAL.

Selected Features

1. Heitler-London-Slater-Pauling (HLSP) functions in the classical valence bond methods.

2. many types of ab initio VB methods, which can describe both static and dynamic correlation.

3. hybrid valence bond method calculations, such as DFVB, VBPCM, VBEFP, etc.

4. systems with 24 active electrons and 24 active orbitals up to the limit of current HPC.

Major Events

In 1989, a pilot VB code based on symmetric group approach (SGA) was developed and applied to H3 molecule. 

In 1992, a novel algorithm based on the left coset decomposition of the symmetric group was proposed and implemented. 

In 1995, the paired-permanent-determinant (PPD) approach was introduced to evaluate the Hamiltonian matrix efficiently, especially for systems comprising many covalent bonds. 

In 1999, Xiamen-99, which is the predecessor of the XMVB code, was released. 

In 2003, the program is released under the XMVB name for the first time as Version 1.0. 

In 2005, the parallel version of XMVB was released. 

In 2007, the distribution of XMVB as a module was incorporated in the GAMESS-US package. 

In 2012, Version 2.0 was released; new features: RDM-based VBSCF with analytical gradient; Fock-based VBSCF with analytical gradient; VBPT2, dc-DFVB, VBPCM and VBEFP.

In 2015, Version 2.1 was released; new features: Hessian-based VBSCF; OpenMP parallelization.

In 2017, Version 3.0 was released; new features: Cholesky Decomposition; Tensor-based VBSCF; hc-DFVB.