The recovery of valuable minerals in froth flotation industry relies on finding inexpensive and environmentally friendly reagents that selectively adsorb upon surfaces and interfaces. Computer simulations, especially when extended, provide access to detailed mechanistic information on solvent configurations and may ascertain crucial dynamical events over the adsorption process. Further, in silico throughput screening can prevent both the high cost of experiments and the related risks to the environment. Yet, a better compromise between accuracy and computational cost must be met. Machine learning (ML) simulations may ease the latter and suggest solidophilic reagents able to improve the flotation efficiency, shedding new light on discerning descriptors able to accurately capture the nature of the molecule-surface interaction. In this work, our recent advancements in modeling of new accurate mineral-water interfaces based on active learning of ab initio molecular dynamics trajectories have been introduced. The case of some habitual oxides and minerals liberated in mining industry will be taken as examples.

中文翻译：

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用于优化浮选选择性的增强机器学习分子模拟：一篇透视论文


在泡沫浮选行业中，有价值矿物的回收依赖于寻找选择性吸附在表面和界面上的廉价且环保的试剂。计算机模拟，尤其是扩展后，可以访问有关溶剂配置的详细机理信息，并可以确定吸附过程中的关键动力学事件。此外，计算机通量筛选可以防止高昂的实验成本和对环境的相关风险。然而，必须在精度和计算成本之间实现更好的折衷。机器学习 （ML） 模拟可以缓解后者，并表明亲固体试剂能够提高浮选效率，为能够准确捕捉分子-表面相互作用性质的敏锐描述符提供了新的思路。在这项工作中，我们介绍了基于从头计算分子动力学轨迹的主动学习的新型精确矿水界面建模的最新进展。以采矿业中释放的一些惯性氧化物和矿物为例。