Projects per year
Organization profile
Organization profile
The Nanoporous Materials Genome Center (NMGC) discovers and explores microporous and mesoporous materials, including metal-organic frameworks (MOFs), zeolites, and porous polymer networks (PPNs). These materials find use as separation media and catalysts in many energy-relevant processes and their next generation computational design offers a high-payoff opportunity. Towards that end, the NMGC develops state-of-the-art predictive modeling tools and employs them to increase the pace of materials discovery. The NMGC provides a repository of experimental and predicted structures and associated properties for the rapidly growing scientific communities that are interested in using these materials in energy-relevant technologies.
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Collaborations and top research areas from the last five years
Profiles
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Melanie F Burns
- Mechanical Engineering - Research Professional 6
- Advanced Technologies for Preservation of Biological Systems
- Nanoporous Materials Genome Center - Managing Director
- Inorganometallic Catalyst Design Center - Managing Director
Person: Research Support, Executive, Administrative, and Managerial Staff
Projects
- 2 Finished
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NMGC: Nanoporous Materials Genome: Methods and Software to Optimize Gas Storage, Separations, and Catalysis (Phase 2)
Siepmann, I. (PI), Cramer, C. (CoI), Gagliardi, L. (CoI), Truhlar, D. G. (CoI), Tsapatsis, M. (CoI) & Goodpaster, J. D. (CoI)
U.S. DEPARTMENT OF ENERGY (USDOE)
9/1/17 → 8/31/21
Project: Research project
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NMGC: Nanoporous Materials Genome: Methods and Software to Optimize Gas Storage, Separations, and Catalysis (Phase 1)
Siepmann, I. (PI), Cramer, C. (CoI), Gagliardi, L. (CoI), Truhlar, D. G. (CoI), Tsapatsis, M. (CoI) & Goodpaster, J. D. (CoI)
9/1/12 → 8/31/17
Project: Research project
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Analytic Nuclear Gradients for Complete Active Space Linearized Pair-Density Functional Theory
Hennefarth, M. R., Hermes, M. R., Truhlar, D. G. & Gagliardi, L., May 14 2024, In: Journal of Chemical Theory and Computation. 20, 9, p. 3637-3658 22 p.Research output: Contribution to journal › Article › peer-review
3 Scopus citations -
CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces
Kirkvold, C., Collins, B. A. & Goodpaster, J. D., Jul 4 2024, In: Journal of Physical Chemistry Letters. 15, 26, p. 6791-6797 7 p.Research output: Contribution to journal › Article › peer-review
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Efficient method for twist-averaged coupled cluster calculation of gap energy: Bulk study of stannic oxide
Shaban Tameh, M., Gladfelter, W. L. & Goodpaster, J. D., Sep 1 2024, In: AIP Advances. 14, 9, 095106.Research output: Contribution to journal › Article › peer-review
Open Access
Datasets
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Huzinaga Projection Embedding for Efficient and Accurate Energies of Systems with Localized Spin-densities
Graham, D. S., Wen, X., Chulhai, D. & Goodpaster, J. D., Data Repository for the University of Minnesota, May 7 2021
https://hdl.handle.net/11299/219605
Dataset
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Data for "Ground State Absolutely Localized WF-in-DFT Embedding Energies"
Graham, D. S., Wen, X., Chulhai, D. & Goodpaster, J. D., Data Repository for the University of Minnesota, 2019
DOI: 10.13020/r7c0-2x97, http://hdl.handle.net/11299/208808
Dataset
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Supporting Data for "From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains"
Shen, Z., Chen, J., Vernadskaia, V., Ertem, S. P., Mahanthappa, M., Hillmyer, M. A., Reineke, T. M., Lodge, T. P. & Siepmann, I., Data Repository for the University of Minnesota, May 11 2020
DOI: 10.13020/7zcr-w347, https://hdl.handle.net/11299/214077 and one more link, http://hdl.handle.net/11299/214077 (show fewer)
Dataset