Abstract: Despite remarkable recent progress in linear-scaling density function theory, the computational cost of existing methods remains too high for routine ab initio molecular dynamics (AIMD) simulations. We developed a new linear-scaling AIMD method with an extremely low computational overhead by assuming that electrons in materials are strictly localized within predefined radii. High efficiency of the method is achieved without sacrificing its accuracy with a combination of two techniques: (1) fast but only approximate description of localized electrons and (2) the stochastic treatment of nuclear motion, fine-tuned to retain stable dynamics even with imperfect forces. A remarkable feature of the implemented method is that it remains efficient for challenging condensed phase systems even if large accurate basis sets are used. We demonstrated that, for systems well-represented by localized electrons, the new AIMD method enables simulations on previously inaccessible time and length scales.
Bio: Mathieu Lavallée-Adam is an assistant professor at the University of Ottawa in the Department of Biochemistry, Microbiology and Immunology and is affiliated to the Ottawa Institute of Systems Biology. He performed his postdoctoral research in John R. Yates III’s laboratory in the Department of Chemical Physiology at The Scripps Research Institute. He obtained a B.Sc. in Computer Science and a Ph.D. in Computer Science, Bioinformatics option, from McGill University under the supervision of Mathieu Blanchette and Benoit Coulombe. His research focuses on the development of statistical and machine learning algorithms for the analysis of quantitative proteomics, intact protein mass spectrometry and protein-protein interaction network datasets. In addition to obtaining funding from NSERC and Genome Canada, Dr. Lavallée-Adam is also a recipient of the John Charles Polanyi Prize in Chemistry and of the Canadian Vanier scholarship recognizing his research and involvement in the community. He also co-organized summer camps for high school students on the applications of computer science in biomedical sciences.