首页|Data on Machine Learning Reported by Chen Qu and Colleagues [Formic Acid-Ammonia Heterodimer: A New D-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer]
Data on Machine Learning Reported by Chen Qu and Colleagues [Formic Acid-Ammonia Heterodimer: A New D-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer]
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New research on Machine Learning is the subject of a report. According to news reporting from Toronto, Canada, by NewsRx journalists, research stated, “The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined.” The news correspondents obtained a quote from the research, “Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported. Here, we report a full-dimensional potential energy surface (PES) for this complex, using two closely related D-machine learning methods to bring it to the CCSD(T) level of accuracy. The PES dissociates smoothly and accurately. Using a 2d quantum model the ground vibrational-state tunneling splitting is estimated to be less than 10 cm.”
TorontoCanadaNorth and Central AmericaAcyclic AcidsAmmoniaCyborgsEmerging TechnologiesFormic AcidsMachine LearningNitrogen Compounds
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