Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 14, Issue 5, Pages 2771-2783Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.8b00089
Keywords
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Funding
- Deutsche Forschungsgemeinschaft [SFB 1114/A04]
- European Commission ERC starting grant pcCell [307494]
- National Science Foundation [CHE-1265929, CHE-1738990, PHY-1427654]
- Welch Foundation [C-1570]
- ARO grant [W911NF-15-1-0316]
- AFOSR grant [FA9550-14-1-0088]
- ONR grant [N00014-17-1-2551]
- Big-Data Private Cloud Research Cyberinfrastructure MRI-award (NSF grant) [CNS-1338099]
- clusters of the Department of Mathematics and Computer Science at Freie Universitat Berlin.
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1265929] Funding Source: National Science Foundation
- European Research Council (ERC) [307494] Funding Source: European Research Council (ERC)
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Recent methods for the analysis of molecular kinetics from massive molecular dynamics (MD) data rely on the solution of very large eigenvalue problems. Here we build upon recent results from the field of compressed sensing and develop the spectral oASIS method, a highly efficient approach to approximate the leading eigenvalues and eigenvectors of large generalized eigenvalue problems without ever having to evaluate the full matrices. The approach is demonstrated to reduce the dimensionality of the problem by 1 or 2 orders of magnitude, directly leading to corresponding savings in the computation and storage of the necessary matrices and a speedup of 2 to 4 orders of magnitude in solving the eigenvalue problem. We demonstrate the method on extensive data sets of protein conformational changes and protein ligand binding using the variational approach to conformation dynamics (VAC) and time lagged independent component analysis (TICA). Our approach can also be applied to kernel formulations of VAC, TICA, and extended dynamic mode decomposition (EDMD).
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