Twice today that question was put to me. I thought I’d take a moment and answer it not just for the two individuals who asked, but for anyone who might be wondering. Below is my recommendation…
The Computational Chemistry Comparison and Benchmark Database (CCCBDB) contains links to experimental and computational thermochemical data for a selected set of gas-phase atoms and molecules as well as tools for comparing experimental and computational ideal-gas thermochemical properties.
PyQuante is an open-source suite of programs for developing quantum chemistry methods. The program is written in the Python programming language, but has many "rate-determining" modules also written in C for speed.
NWChem is an electronic structure package that features MC-SCF, MPn, CC, CI, and DFT methods. Properties, solvation models, QM/MM, and MD simulations are also possible.
GAMESS-US is a full-featured electronic structure software package with MC-SCF, CC, DFT, and CI wave functions. QM/MM, FMO, solvation, and MD calculations are also possible.
To make a density map from a 2D set of data, the first step is to compute values for the third dimension. (Gnuplot has no facilities for computing these values automatically.) The simplest way is to make a 2D histogram; the plot is divided in small 2D regions, and the z-values are proportional to the number of points inside these regions. The following Python script will make an histogram from a time series of two dihedral angles.
P. Woodland, J. Odell, V. Valtchev, und S. Young. Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2, Seite 125-128. Adelaide, Australia, (April 1994)
A. Poritz. Proceedings of the 1982 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7, Seite 1291-1294. Paris, France, (Mai 1982)
G. Hofer, und K. Richmond. Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH), Seite 454-457. Makuhari, Japan, (September 2010)
W. Yan, A. Vangipuram, P. Abbeel, und L. Pinto. (2020)cite arxiv:2003.05436Comment: Project website: https://sites.google.com/view/contrastive-predictive-model.
S. Günnemann, B. Boden, und T. Seidl. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Seite 565--580. Springer, (2011)
A. Islam, M. Islam, M. Alam, und S. Ullah. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 1 (4):
01-13(Oktober 2011)
T. Vijayakumar, V.Nivedhitha, K.Deeba, und M. Bama. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2 (1):
35-43(Februar 2012)
S. Lilly, und C. Carollo. (2016)cite arxiv:1604.06459Comment: 17 pages of text plus 13 figures. Submitted to The Astrophysical Journal on April 18 2016.
G. Krempl. Proc. of the 1st Int. Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) co-located with ECML PKDD 2015, 1425, CEUR Workshop Proceedings, (2015)