Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol⁻¹) of Li⁺ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li⁺ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li⁺ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.

中文翻译：

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提高卤水开采中锂离子回收率：集成下一代情感人工智能和可解释的机器学习来预测基于冠醚的分层纳米材料的吸附能

人工智能（AI）正在盐水开采中应用，通过提高回收效率和减少能源消耗来提高对锂离子电池制造至关重要的锂的提取。提出了一种结合情感神经网络（ENN）和随机森林（RF）算法的创新方法，利用掺入冠醚（CE）的蜂窝状二维纳米材料来阐明Li ^{+离子的吸附能（AE）（kcal mol -1} ）。通过密度泛函理论 (DFT) 和高斯 16 模拟对蜂窝状二维材料和单个 CE 进行筛选和特征工程分析。所选的蜂窝状图案二维材料包括石墨烯、硅烯和六方氮化硼，而评估的具体CE是15-crown-5和18-crown-6。冠钝化的二维表面通过范德华力保持重要的吸附位点，以有效回收锂^离子。新奥预测目标吸附位点精度高、偏差小。还探索了基于 eTAI (XAI) 的 Shapley Additive exPlanations (SHAP)，以深入了解 CE 嵌入的 2D 纳米材料对于回收 Li ⁺离子的特征重要性。极端梯度增强算法 (XGBoost) 模型在特征工程分析中显示 RT-2-MAPE = 0.4618% 和 ENN-2-MAPE = 0.4839%。这项研究将深入了解人工智能驱动的纳米技术，该技术为通过盐水开采提取自然资源提供了一种可行且可持续的方法。