首页|Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machine Learning [SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations]
Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machine Learning [SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is the subject of a report. According to newsreporting out of Lausanne, Switzerland, by NewsRx editors, research stated, “Recently, we introduceda class of molecular representations for kernel-based regression methods the spectrum of approximatedHamiltonian matrices (SPAM) that takes advantage of lightweight one-electron Hamiltonians traditionallyused as a self-consistent field initial guess. The original SPAM variant is built from occupied-orbital energies(i.e., eigenvalues) and naturally contains all of the information about nuclear charges, atomic positions,and symmetry requirements.”
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