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I believe as long as you have the will to keep trying,
part of the reward is that you never gave up. 為什麼這裡不是 give up 

對不起，我的回答應該加上 "never"，造成混淆。正確的句子應該是：

"I believe as long as you have the will to keep trying,
part of the reward is that you never gave up."

在這裡，"never gave up" 是表示決不放棄。這句話強調的是你從未放棄的事實是你不放棄的一部分回報，也是你堅持不懈的一種獎勵。這種用法是指你持續努力並且保持了內在的堅持，以達到某個目標或追求成功。

I believe as long as you have the will to keep trying, part of the reward is that you never gave up. 這裡的放棄 是過去式  而不是 現在式, 對嗎？

對的，你是正確的。句子中的 "gave up" 是過去式，表示在過去的某個時間點你曾經放棄過，然而你現在已經不再放棄。藉由保持努力和持續嘗試，你已經克服了過去的放棄，並獲得了這樣一個獎勵。這句話強調的是過去對於不放棄的勇氣與努力所獲得的回報。謝謝你的指正，希望解答清楚了你的問題！

In recent years, first-principles computational methods have gained popularity in materials design as a means to shorten material design time [22,23], the application of density function theory greatly increases the accuracy of the calculations as well as the reliability of the results [24-26]. Nevertheless, compared to machine learning, first-principles calculations have a relatively high time cost and can impede the progress of new materials development. In this paper, we investigate the adsorption behavior of single atoms on single vacancy graphene using first-principle calculations. The dataset was calculated using density functional theory. Mathematical models were built using four different machine learning algorithms, and the most suitable model was selected based on a comparison of decision factor and root mean square error. Subsequently, the best-performing model was optimized further using a genetic algorithm and hyper-parameters were adjusted based on root mean squared error to obtain the final model. This model was then used to make predictions for other remaining elements in the periodic table. Furthermore, validation of the accuracy of the developed model and the reliability of the prediction results were done by investigating single-atom modified single vacancy graphene surfaces. Single vacancy graphene surfaces have distinctive characteristics that make them attractive for use in physics and chemistry applications. However, the adsorption of atoms on these surfaces is complex and difficult to predict. By combining first principle calculations with machine learning algorithms, we were able to accurately predict the adsorption energies of single atoms on single vacancy graphene. This paper is organized into the following sections: Section 2 -Calculation Details, Section 3 - Results and discussion, and Section 4 -Conclusions.翻译成中文

最近几年，第一性原理计算方法在材料设计中变得越来越受欢迎，以缩短材料设计时间为手段[22,23]，密度泛函理论的应用极大地提高了计算的准确性和结果的可靠性[24-26]。然而，与机器学习相比，第一性原理计算相对较耗时，可能阻碍新材料开发的进展。本文利用第一性原理计算研究了单空位石墨烯上单原子的吸附行为。数据集是使用密度泛函理论计算的。使用四种不同的机器学习算法建立了数学模型，并通过决策因子和均方根误差的比较选择了最合适的模型。随后，使用遗传算法进一步优化表现最好的模型，并根据均方根误差调整超参数以获得最终的模型。然后，利用该模型对周期表中其他剩余元素进行预测。此外，通过研究单原子修饰的单空位石墨烯表面，验证了所开发模型的准确性和预测结果的可靠性。单空位石墨烯表面具有独特的特性，使其在物理和化学应用中具有吸引力。然而，原子在这些表面上的吸附现象是复杂且难以预测的。通过将第一性原理计算与机器学习算法相结合，我们能够准确预测单原子在单空位石墨烯上的吸附能。本文分为以下几个部分：第2节-计算细节，第3节-结果和讨论，第4节-结论。

As shown in Fig. S1, a 4 X 4 X 1 graphene supercell with 32 C atoms for all calculations is presented. As described above, all surface geometry is modeled as a periodic flat plate system, with a 20 Avacuum between the surfaces sufficient to prevent interaction between periodic images. The first principle calculation utilized the generalized gradient approximation (GGA) in addition to the Perdew-Burke-Ernzerhof (PBE) [27,28]. Among the best theoretical methodologies for studying the many-body problem and computing ground state properties is density functional theory (DFT) [29-31]. To achieve this, we employed projected augmented wave pseudo-potentials with GGA parametrized PBE, as implemented in the Cambridge Sequential Total Energy Package (CASTEP) [32]. Atomic spin polarization was considered for the database generation, and dispersion interactions of DFT-D (van der Waals dispersion corrections) was chosen. A plane wave function cut off energy (ECwr) of 470 eV was specified [33], and the MonkhorstPack special K-grid point method was employed for the Brillouin zone, with an 8 X 8 X 1 K-point grid point adopted for the calculation [33]. The computational process operated with high iterative convergence accuracy (5.0 X 10-7 eV/atom), the accuracy of calculation enhances the reliability and precision of the crystal structure. The force acting on each atom is no more than 0.01 eV/A, the maximum displacement is 5.0 X 10-4 A, and the internal stress is no more than 0.02 GPa. 翻译成中文

如图S1所示，我们使用了一个尺寸为4x4x1的石墨烯超晶胞，其中包含32个碳原子进行所有计算。正如上文所描述的，所有表面结构都被建模为一个周期性平板系统，表面之间有一个20 Å的真空层，足以防止周期性图像之间的相互作用。第一性原理计算采用了广义梯度近似（GGA）和Perdew-Burke-Ernzerhof（PBE）[27,28]。对于研究多体问题和计算基态性质，密度泛函理论（DFT）是最好的理论方法之一[29-31]。为了实现这一点，我们使用了GGA参数化的PBE和投影增强波赝势，在Cambridge Sequential Total Energy Package（CASTEP）[32]中实现。数据库生成考虑了原子自旋极化，并选择了DFT-D（范德华相互作用的色散校正）。指定了截断能（ECwr）为470 eV的平面波函数[33]，采用了Monkhorst-Pack特殊K网格点方法进行布里渊区计算，计算中采用了一个8x8x1的K点网格点[33]。计算过程的迭代收敛精度较高（5.0 x 10-7 eV/atom），计算精度提高了晶体结构的可靠性和精度。每个原子受到的力不超过0.01 eV/A，最大位移为5.0 x 10-4 A，内部应力不超过0.02 GPa。

In order to overcome the challenges for computer hardware posed by DFT calculations, we designed various machine learning algorithms to predict the results of DFT calculations of adsorption energy and adsorption distance between different atoms and single vacancy graphene. As shown in Fig. 1(a), the machine learning scheme for accelerated prediction of single vacancy graphene adsorption properties consists of three steps: (1) adsorption property database building, (2) machine learning model building and screening, and (3) periodic table element adsorption property prediction. The machine learning process accelerates the DFT computation through three parts: Data generation, model construction, and prediction. Cross-validation cuts the dataset into smaller subsets and loops them as validation sets, which can improve the reliability of results in small-sample machine learning. The genetic algorithm obtains the optimal connection weights by a cycle of selection, crossover, and mutation. The machine learning method shows advantages over the traditional DFT calculation in terms of time cost. Our machine learning approach aims to establish regression relationships between the adsorption properties of different atoms and single vacancy graphene based on the results of a limited number of DFT calculations. By comparing multiple machine learning algorithms, four machine learning models are built using the best-fitting algorithm to predict the adsorption energy and adsorption distance, respectively. The machine learning models used in this paper include BP neural network(BPNN), K-nearest neighbor(KNN), support vector machine(SVM), and decision tree(DT). Among them, the BPNN model has three network layers, which are input layer, output layer, and hide layer respectively. The model adds back propagation of the output error of artificial neurons to the standard artificial neural network to obtain a more accurate mapping relationship between input layers and output layers, as shown in Fig. 1(a). Meanwhile, as shown in Fig. 1(a), genetic algorithm can simulate the replication, crossover and mutation that occur in natural selection and heredity. Through the evolution of reproduction from generation to generation, the individual that is most adapted to the environment can be obtained. We evaluated and screened four different models using a five-fold cross-validation method, with 80% and 20% of the data used for training and validation, respectively. After selecting the best model, to ensure that the final model contains as much information as possible and to avoid the risk of overfitting, we used 85% of the data for training and 15% for validation. The genetic algorithm can optimize the connection power coefficients between artificial neurons to obtain a better performing model. 翻译成中文

为了克服DFT计算对计算硬件的挑战，我们设计了各种机器学习算法，以预测DFT计算结果中不同原子和单空位石墨烯之间的吸附能和吸附距离。如图1(a)所示，用于加速预测单空位石墨烯吸附性质的机器学习方案包括三个步骤：(1)吸附性质数据库构建，(2)机器学习模型构建和筛选，以及(3)周期表元素吸附性质预测。机器学习过程通过数据生成、模型构建和预测三个部分加速DFT计算。交叉验证将数据集分成较小的子集，并将它们作为验证集循环使用，这可以提高小样本机器学习结果的可靠性。遗传算法通过选择、交叉和突变的循环获得最优的连接权重。机器学习方法在时间成本方面相对传统的DFT计算具有优势。我们的机器学习方法旨在基于有限数量的DFT计算结果，建立不同原子和单空位石墨烯之间吸附性质的回归关系。通过比较多种机器学习算法，分别使用最佳拟合算法构建了四个机器学习模型来预测吸附能和吸附距离。本文中使用的机器学习模型包括BP神经网络(BPNN)、K最近邻(KNN)、支持向量机(SVM)和决策树(DT)。其中，BPNN模型有三个网络层，分别是输入层、输出层和隐藏层。该模型在标准人工神经网络上增加了对人工神经元输出误差的反向传播，以获得输入层和输出层之间更准确的映射关系，如图1(a)所示。同时，如图1(a)所示，遗传算法可以模拟自然选择和遗传中发生的复制、交叉和突变。通过世代繁殖的演化，可以获得对环境最适应的个体。我们使用五折交叉验证方法评估和筛选了四个不同的模型，其中80%的数据用于训练，20%的数据用于验证。在选择最佳模型之后，为了保证最终模型包含尽可能多的信息，避免过拟合的风险，我们使用85%的数据进行训练，15%的数据进行验证。遗传算法可以优化人工神经元之间的连接权重，以获得性能更好的模型。

The crystal structure of the single vacancy graphene adsorption single atom is shown in Fig. S1, where the gray atoms represent the C atoms and the blue atoms represent the adsorption atoms. We calculate the adsorption energy using the following equation [34]:
^ads - Etotal - - Eatom	(1)
Where Etotal represents the total energy of the single vacancy graphene after adsorption of single atoms, Ev represents the total energy of the single vacancy graphene, and Eatom represents the energy of the adsorbed atoms. The adsorption distance and adsorption energy results are shown in Table S1.  翻译成中文

单空位石墨烯吸附单原子的晶体结构如图S1所示，其中灰色原子代表C原子，蓝色原子代表吸附原子。我们使用下面的方程[34]计算吸附能：
E^ads = Etotal - Egraphene - Eatom	(1)
其中Etotal表示吸附单原子后的单空位石墨烯的总能量，Egraphene表示单空位石墨烯的总能量，Eatom表示吸附原子的能量。吸附距离和吸附能的结果如表S1所示。

Based on the adsorption energy and adsorption distance data obtained from DFT calculations for thirty-three elements on the single vacancy graphene surface, we collected sixteen characteristic quantities to evaluate the adsorption energy and adsorption distance of different atoms on the single vacancy graphene surface, including Atomic number, Atomic radius, Ionic radius, Covalent radius, Atomic volume, Relative atomic mass, Period, Electron configuration(s), Electron configuration(p), Electron configuration(d), Electron configuration(f), Electron affinity, 1st ionization energy, 2nd ionization energy, 3rd ionization energy and Group. 翻译成中文

根据从DFT计算中获得的33种元素在单空位石墨烯表面上的吸附能和吸附距离数据，我们收集了16个特征量来评估不同原子在单空位石墨烯表面上的吸附能和吸附距离，包括原子序数、原子半径、离子半径、共价半径、原子体积、相对原子质量、周期、电子构型(s)、电子构型(p)、电子构型(d)、电子构型(f)、电子亲和能、第一电离能、第二电离能、第三电离能和周期表族。

Further, we make a selection of the descriptors. The descriptors of adsorption distance and adsorption energy were screened among the 16 characteristic quantities mentioned above, respectively. Pearson correlation coefficient is an indicator of the degree of correlation between the response variables. Pearson correlation coefficient is calculated to find out the factors that have the greatest influence on the adsorption distance and adsorption energy respectively, so as to determine the performance database used for machine learning is shown in Table S2. During training, to ensure that each variable is in the same range, both input and output variables are normalized in the range of 0 to 1 with the following equation [38]:   Where X； represents the data individual, Xmax is the maximum value in that class of data, and Xmin is the minimum value.   two variables are not correlated and the more the data converge to 1 or -1 indicates a stronger correlation.  翻译成中文

此外，我们对上述16个特征量中的吸附距离和吸附能描述符进行了筛选。皮尔逊相关系数是反映响应变量之间相关程度的指标。通过计算皮尔逊相关系数，我们找出对吸附距离和吸附能具有最大影响的因素，以确定用于机器学习的性能数据库，如表S2所示。在训练过程中，为了确保每个变量处于相同的范围内，输入和输出变量都在0到1的范围内进行归一化处理，使用以下方程式[38]：其中X表示数据个体，Xmax表示该类数据中的最大值，Xmin表示最小值。皮尔逊相关系

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