Spec Libraries

SPEC libraries are hosted on GitHub at the URL https://github.com/spec-org

The following libraries are available


The Green’s Function Coupled Cluster Library (GFCC) is designed for the Green's function calculation of a molecular system at the Coupled-Cluster level. The library is developed in the C++ programming language with a framework that enables scalability via the scalable design of the algorithm, efficiency via multi-layer parallelism, memory management, scheduling of tensor operations, and flexibility by providing a long-term sustainable platform for the development of the class of methods on machines ranging from workstations to modern supercomputers.


version 1.0 (released 09-27-2019)

  • Concurrent computation of frequency-dependent Green's function matrix elements and spectral function in the CCSD/GFCCSD level (enabled via MPI process groups)
  • Support for multidimensional real/complex hybrid tensor contractions and slicing on both CPU and GPU
  • On-the-fly Cholesky decomposition for atomic-orbital based two-electron integrals
  • Direct inversion of the iterative subspace (DIIS) is customized and implemented as the default complex linear solver
  • Gram-Schmidt orthogonalization for multidimensional complex tensors
  • Model-order-reduction (MOR) procedure for complex linear systems
  • Automatic resource throttling for various inexpensive operations
  • Checkpointing (or restarting) calculation employing parallel IO operations for reading (writing) tensors from (to) disk

version 1.1 (released 06-04-2020)

  • The default linear solver has been changed from DIIS to Generalized Minimal REsidual Method (GMRES)
  • Load balancing across concurrent computation of different orbitals for a given frequency
  • TAMM (Tensor Algebra for Many-body Methods): Improved distributed tensor contraction performance on GPUs

B. Peng, A. Panyala, K. Kowalski, S. Krishnamoorthy, “GFCCLib: Scalable and Efficient Coupled-Cluster Green's Function Library for Accurately Tackling Many Body Electronic Structure Problems”, Computer Physics Communications 265, pp. 108000 (2021); DOI:10.1016/j.cpc.2021.108000

B. Peng, K. Kowalski, A. Panyala, S. Krishnamoorthy, "Green’s function coupled cluster simulation of the near-valence ionizations of DNA-fragments", The Journal of Chemical Physics 152, pp 011101 (2020); https://dx.doi.org/10.1063/1.5138658


Original repository: https://github.com/spec-org/MC-MPn-Direct

version 1.0 (released 06-09-2020)

The MC-MP methods are the Monte Carlo version of MP2, MP3, and MP4. Similarly, the MC-GF methods are the Monte Carlo version of second- and third-order green's function theory. F12 methods perform explicitly correlated MP2 and GF2 calculations. The acronyms correspond to:

  • MC-MP2: second-order Monte Carlo perturbation theory
  • MC-MP3: third-order Monte Carlo perturbation theory
  • MC-MP4: fourth-order Monte Carlo perturbation theory
  • MC-GF2: second-order Monte Carlo Green's function theory
  • MC-GF3: third-order Monte Carlo Green's function theory
  • MC-MP2-F12: explicitly correlated second-order Monte Carlo perturbation theory
  • MC-GF2-F12: explicitly correlated second-order Monte Carlo Green's Function theory

A. E. Doran and So Hirata, "Monte Carlo Second- and Third-Order Many-Body Green’s Function Methods with Frequency-Dependent, Nondiagonal Self-Energy", Journal of Chemical Theory and Computation 15, pp. 6097-6110 (2019); https://dx.doi.org/10.1021/acs.jctc.9b00693


Original repository: https://urania.chem.washington.edu/chronusq/x2chem.git

(version 1.0, released 05/03/2021)

An eXact 2-Component relativistic quantum chemistry library. This library provides a convenient tool that wraps X2C transformation modules with an interface to user-defined integral engines. The output of this library is the X2C transformed relativistic two-component Hamiltonian.


Original repository: https://github.com/wavefunction91/MACIS

The Many-Body Adaptive Configuration Interaction Suite (MACIS) is a modern, modular C++ library for high-performance quantum many-body methods based on configuration interaction (CI). MACIS currently provides a reuseable and extentible interface for the development of full CI (FCI), complete active-space (CAS) and selected-CI (sCI) methods for quantum chemistry. Efforts have primarily been focused on the development of distributed memory variants of the adaptive sampling CI (ASCI) method on CPU architectures, and work is underway to extend the functionality set to other methods commonly encountered in quantum chemistry and to accelerator architectures targeting exascale platforms.

D. B. Williams-Young, N. M. Tubman, C. Mejuto-Zaera, W. A. de Jong "A parallel, distributed memory implementation of the adaptive sampling configuration interaction method", The Journal of Chemical Physics 158, pp. 214109 (2023); https://dx.doi.org/10.1063/5.0148650


Scalable DMRG: Massively parallel quantum chemical density matrix renormalization group library.