<\/p>\n
In the McMahon Research Group, we model condensed matter\u00a0starting\u00a0from the fundamental equations of quantum mechanics. Except for the most simple systems, these are impossible to solve analytically. We therefore apply computational methods; typically, we use either quantum Monte Carlo (QMC), which can (in principle) find exact solutions,\u00a0or density-functional theory (DFT), which is approximate, but with a much lower computationally cost.<\/p>\n
The computational cost of QMC and\/or accuracy of\u00a0DMC, however, limit their applicability. We therefore look for ways in which to improve the accuracy and efficiency of quantum simulation methods.\u00a0Of recent interest is the use of machine learning<\/strong> in computational physics.<\/p>\n<\/p><\/div>\n<\/section>\n <\/p>\n <\/p>\n <\/p>\n Image coming soon<\/strong><\/p>\n<\/p><\/div>\n <\/p>\n Machine Learning<\/strong><\/span><\/p>\n There are several problems\u00a0in computational physics for which\u00a0accurate and efficient algorithms for their solution are unknown. A recent interest of ours has\u00a0been to consider ways in which techniques and theory in and from the emerging field of data science\u00a0can be used to solve such open, currently intractable problems.\u00a0An example is the approximation to the universal, but unknown exchange–correlation density functional in DFT. We have recently developed a method based on deep learning (machine learning) can be used to determine the functional form of this unknown functional (manuscript in preparation).<\/p>\n <\/p>\n <\/p>\n<\/p><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":" <\/p>\n <\/p>\n In the McMahon Research Group, we model condensed matter\u00a0starting\u00a0from the fundamental equations of quantum mechanics. Except for the most simple systems, these are impossible to solve analytically. We therefore apply computational methods; typically, we use either quantum Monte Carlo (QMC), which can (in principle) find exact solutions,\u00a0or density-functional theory (DFT), which is approximate, but with a much lower computationally cost.<\/p>\n The computational cost of QMC and\/or accuracy of\u00a0DMC, however, limit their applicability. We therefore look for ways in which to improve the accuracy and efficiency of quantum simulation methods.\u00a0Of recent interest is the use of machine learning<\/strong> in computational physics.<\/p>\nMethods Development<\/h2>\n