Hydrogen Combustion. A Benchmark Data Set for Hydrogen Combustion (2021). Akshaya Das*, Christopher J. Stein*, Farnaz Heidar-Zadeh*, Luke Bertels*, Meili Liu, Xingyi Guan, Mojtaba Haghighatlari, Jie Li, Oufan Zhang, Hongxia Hao, Itai Leven, Martin Head-Gordon, Teresa Head-Gordon (submitted).
Data that was developed for machine learning applications deposited here: https://github.com/THGLab/H2COMBUSTION_DATA. All the python scripts used to generate coordination number based PES surface to analyze the data for each reaction channel is also present in https://github.com/THGLab/H2Combustion/tree/coord_nums/combust
X-EISD Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States (2020). James Lincoff, Mojtaba Haghighatlari, Mickael Krzeminski, João M.C. Teixeira,
Gregory-Neal W. Gomes, Claudiu C. Gradinaru, Julie D. Forman-Kay,
Teresa Head-Gordon Chem. Comm. 3, Article no: 74
Data that support the development of X-EISD have been deposited at https://github.com/THGLab/X-EISD.
In addition, the code and a command-line interface are available in the same repository for the reproducibility of reported results and user accessibility for future studies.
UCBShift: Accurate Prediction of Chemical Shifts for Aqueous Protein Structure on “Real World” Data (2020). Jie Li, Kochise C. Bennett, Yuchen Liu, Martin V. Martin, and T. Head-Gordon. Chem. Sci. 11, 3180-3191
MB-UCB: A. K. Das, L. Urban, I. Leven, M. Loipersberger, A. Aldossary, M. Head-Gordon, T. Head-Gordon (2019). Development of an Advanced Force Field for Water using Variational Energy Decomposition Analysis. J. Chem. Theory Comput. 15 (9), 5001-5013 [link]
This file contains the MB-UCB parameters: reportv7
MR-3D-DenseNet. S. Liu, J. Li, K. C. Bennett, B. Ganoe, T. Stauch, M. Head-Gordon, A. Hexemer, D. Ushizima, and T. Head-Gordon (2019) Multiresolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography. J. Phys. Chem. Lett. 10, 4558−4565. DOI: 10.1021/acs.jpclett.9b01570.
PB-AM and PB-SAM. E. Jurrus, D. Engel, K. Star, K. Monson, J. Brandi, L. E. Felberg, D.H. Brookes, L. Wilson, J. Chen, K. Liles, M. Chun, P. Li, T. Dolinsky, R. Konecny, D. Koes, J. E. Nielsen, T. Head-Gordon, W. Geng, R. Krasny, M. Gunner, G.-W. Wei, M. J. Holst, J. A. McCammon, N. A. Baker (2018). Improvements to the APBS biomolecular solvation software suite. Protein Sci 27 (1), 112-128. doi: 10.1016/j.str.2014.10.011 ;
Berkeley-SC-Ensemble Decoys. A. Bhowmick, T. Head-Gordon (2015). A monte carlo method for generating side chain structural ensembles. Structure 23(1):44-55. doi: 10.1016/j.str.2014.10.011 ; M. S. Lin, N. L. Fawzi, and T. Head-Gordon (2007). Hydrophobic potential of mean force as a solvation function for protein structure prediction. Structure 15, 727-740 (2007). http://DOI:10.1016/j.str.2007.05.004
Temperature Cool Walking. J. Lincoff, S. Sasmal, and T. Head-Gordon (2016). Comparing generalized ensemble methods for sampling of systems with many degrees of freedom. J. Chem. Phys. 145(17), 174107.http://doi.org/10.1063/1.4965439 S. Brown & T. Head-Gordon (2003). Cool-walking: a new markov chain monte carlo sampling method. J. Comp. Chem. 24 (1), 68-76 (PAK Symposium).http://doi/abs/10.1002/jcc.10181
The code here is a top-level python script that can run TCW simulations using the OpenMM software package. https://github.com/thglab/cool-walking