首页|Research from University of California Provide New Insights into Machine Learnin g (Simulating CO2 diffusivity in rigid and flexible Mg-MOF-74 with machine-learn ing force fields)
Research from University of California Provide New Insights into Machine Learnin g (Simulating CO2 diffusivity in rigid and flexible Mg-MOF-74 with machine-learn ing force fields)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Berkeley, Cal ifornia, by NewsRx editors, research stated, "The flexibility of metal-organic f rameworks (MOFs) affects their gas adsorption and diffusion properties." Funders for this research include National Science Foundation; Prytanean Foundat ion; Alfred P. Sloan Foundation. The news reporters obtained a quote from the research from University of Califor nia: "However, reliable force fields for simulating flexible MOFs are lacking. A s a result, most atomistic simulations so far have been carried out assuming rig id MOFs, which inevitably overestimates the gas adsorption energy. Here, we show that this issue can be addressed by applying a machine-learning potential, trai ned on quantum chemistry data, to atomistic simulations."
University of CaliforniaBerkeleyCali forniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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