首页|Studies Conducted at Virginia Commonwealth University on Machine Learning Recent ly Reported (Improving Second-order Mollerplesset Perturbation Theory for Nonco valent Interactions With the Machine Learning-corrected ab Initio Dispersion ... )
Studies Conducted at Virginia Commonwealth University on Machine Learning Recent ly Reported (Improving Second-order Mollerplesset Perturbation Theory for Nonco valent Interactions With the Machine Learning-corrected ab Initio Dispersion ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting out of Richmond, Virginia, by NewsRx editors, research stated, "In this work, we utilize our recently developed mach ine learning (ML)-corrected ab initio dispersion (aiD) potential, known as D3-ML , which is based on the comprehensive SAPT10K dataset and relies solely on Carte sian coordinates as input, to address the dispersion deficiencies in second-orde r M & oslash;ller-Plesset perturbation theory (MP2) by replacing i ts problematic dispersion and exchange-dispersion terms with D3-ML. This leads t o the development of a new dispersion-corrected MP2 method, MP2+aiD(CCD), which outperforms other spin-componentscaled and dispersion-corrected MP2 methods as well as popular ML models for predicting noncovalent interactions across various datasets, including S66 x 8, NAP6 (containing 6 naphthalene dimers), L7, S12L, DNA-ellipticine, the C-60 dimer, and C-60[6] CPPA." Financial supporters for this research include American Chemical Society, VCU Gr aduate School Dissertation Assistantship.
RichmondVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningVirginia Commonwealth University
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