首页|Xiamen University Reports Findings in Machine Learning (Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batter ies by Machine Learning Potential-Based Molecular Dynamics)
Xiamen University Reports Findings in Machine Learning (Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batter ies by Machine Learning Potential-Based Molecular Dynamics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - New research on Machine Learning is the subject o f a report. According to news originating fromXiamen, People’s Republic of Chin a, by NewsRx correspondents, research stated, “Due to the increasedconcern abou t energy and environmental issues, significant attention has been paid to the de velopmentof large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flowbattery (RFB) is one of the most promising systems.”Our news journalists obtained a quote from the research from Xiamen University, “Recently, the highcost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redoxactiveorganic molecules. To expand the w orking voltage, computational chemistry has been applied tosearch for organic m olecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty i n parametrization whenconsidering the complex solvation of supporting electroly tes. Besides, although ab initio molecular dynamics(AIMD) describes the support ing electrolytes with the same level of electronic structure theory asthe redox couple, the application is impeded by the high computation costs. Recently, mac hine learningmolecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude withoutsacrificing the accuracy. It has been est ablished that redox potentials can be computed by MLMD withtwo separated machin e learning potentials (MLPs) for reactant and product states, which is redundantand inefficient. In this work, an automated workflow is developed to construct a universal MLP for bothstates, which can compute the redox potentials or acidi ty constants of redox-active organic moleculesmore efficiently."
XiamenPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysics
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