化工学报 ›› 2023, Vol. 74 ›› Issue (1): 14-28.DOI: 10.11949/0438-1157.20221077
收稿日期:
2022-07-30
修回日期:
2022-09-05
出版日期:
2023-01-05
发布日期:
2023-03-20
通讯作者:
蓝兴英
作者简介:
周天航(1994—),男,博士,讲师,zhouth@cup.edu.cn
基金资助:
Tianhang ZHOU1(), Xingying LAN1,2(), Chunming XU1,2
Received:
2022-07-30
Revised:
2022-09-05
Online:
2023-01-05
Published:
2023-03-20
Contact:
Xingying LAN
摘要:
广阔的化学空间蕴藏着近乎无限的可能,高性能聚合物材料的设计至今仍是一项充满挑战的工作。利用实验或高通量计算广泛探索大量样本,选择其中性能较好的候选材料进行深入研究的传统试错方式,越发难以在庞大的组合序列中筛选出满足实际多项性能需求的新材料。由先进计算方法、自动化实验和大数据技术耦合形成的新型智能研发模式,突破了“经验指导实验”的传统思路,有望实现在广阔的结构与性质空间中进行聚合物材料的特性预测,逐渐成为克服各类瓶颈问题的得力助手,大大提高了高性能聚合物设计的效率。本文回顾了以往聚合物设计的困境,讨论了人工智能方法的一般思路及其在聚合物设计中的工作原理,列举了采用智能方法在高性能聚合物研发工作中取得突破性进展的典型案例,最后对当前行业发展趋势进行总结,展望了智能设计在新型聚合物研究中的规模化应用前景。
中图分类号:
周天航, 蓝兴英, 徐春明. 人工智能加速聚合物设计的最新进展和未来前景[J]. 化工学报, 2023, 74(1): 14-28.
Tianhang ZHOU, Xingying LAN, Chunming XU. Artificial intelligence for accelerating polymer design: recent advances and future perspectives[J]. CIESC Journal, 2023, 74(1): 14-28.
计算方法 | 可用软件 | 可计算特性 |
---|---|---|
基于密度泛函理论(DFT)的第一性原理计算 | VASP[ | 带隙、介电常数、折射率、吸附能、反应活化能等 |
经典分子动力学模拟(MD)和粗粒化(CG)模拟 | LAMMPS[ | 结构因子、回旋半径、相平衡、黏度、热导率、表面张力、离子扩散、气体渗透等 |
表1 用于聚合物设计的高性能计算方法、相关软件和可计算特性
Table 1 Methods, packages and target properties of computer simulations for polymer design
计算方法 | 可用软件 | 可计算特性 |
---|---|---|
基于密度泛函理论(DFT)的第一性原理计算 | VASP[ | 带隙、介电常数、折射率、吸附能、反应活化能等 |
经典分子动力学模拟(MD)和粗粒化(CG)模拟 | LAMMPS[ | 结构因子、回旋半径、相平衡、黏度、热导率、表面张力、离子扩散、气体渗透等 |
聚合物信息 | 方法 | 原理 |
---|---|---|
分子组成和结构 | 简化分子输入线性输入系统(SMILES)[ | 分子信息被转换成特征向量或张量 |
化学反应 | 反应SMILESs[ | 基于描述分子组成的方法,从反应物的分子描述符 (如指纹)开始,对其进行求和以及加减等操作 |
序列的特征化 | One-hot encoding(OHE)[ | 将聚合物的序列转换为数字表示,如将有两个单体的 共聚物映射成一个“0”和“1”的二进制数字 |
表2 针对分子结构、反应和序列等聚合物信息数字特征化的方法及其原理
Table 2 Features of polymers and their representation methods and principles
聚合物信息 | 方法 | 原理 |
---|---|---|
分子组成和结构 | 简化分子输入线性输入系统(SMILES)[ | 分子信息被转换成特征向量或张量 |
化学反应 | 反应SMILESs[ | 基于描述分子组成的方法,从反应物的分子描述符 (如指纹)开始,对其进行求和以及加减等操作 |
序列的特征化 | One-hot encoding(OHE)[ | 将聚合物的序列转换为数字表示,如将有两个单体的 共聚物映射成一个“0”和“1”的二进制数字 |
算法 | 优势 | 劣势 |
---|---|---|
核脊回归(KRR)、支持向量机 (SVM) | 计算成本低 | 通常不适用于大数据集 |
高斯过程回归(GPR) | 可以很好地预测目标值的不确定性 | 不具备在单个模型中处理多参数问题的能力 |
K-邻近算法(K-nearest neighbors) | 直观和简单 | 所分类别的数量必须人为定义且对性质不均一的数据库效果不佳 |
朴素贝叶斯(naive Bayes, NB) | 直观和简单,适用于大型数据集 | 假设所有特征是独立的,因此只适用于较简单的分类问题 |
随机森林(RF)、梯度提升 (gradient boosting, GB) | 适用于大型数据集,并且可提高每个描述符的重要性,有较强的可解释性 | 在相关的超参优化中,可能会创建过于复杂的决策树结构而导致过度拟合 |
人工神经网络(ANN) | 从大规模数据集中捕捉非线性复杂关系的能力很强 | 需要较多的训练数据且缺乏可解释性 |
表3 用于正向设计的重要机器学习方法及其优缺点[6,26,46-48]
Table 3 A list of important machine learning (ML) methods in forward design and their advantages and disadvantages[6,26,46-48]
算法 | 优势 | 劣势 |
---|---|---|
核脊回归(KRR)、支持向量机 (SVM) | 计算成本低 | 通常不适用于大数据集 |
高斯过程回归(GPR) | 可以很好地预测目标值的不确定性 | 不具备在单个模型中处理多参数问题的能力 |
K-邻近算法(K-nearest neighbors) | 直观和简单 | 所分类别的数量必须人为定义且对性质不均一的数据库效果不佳 |
朴素贝叶斯(naive Bayes, NB) | 直观和简单,适用于大型数据集 | 假设所有特征是独立的,因此只适用于较简单的分类问题 |
随机森林(RF)、梯度提升 (gradient boosting, GB) | 适用于大型数据集,并且可提高每个描述符的重要性,有较强的可解释性 | 在相关的超参优化中,可能会创建过于复杂的决策树结构而导致过度拟合 |
人工神经网络(ANN) | 从大规模数据集中捕捉非线性复杂关系的能力很强 | 需要较多的训练数据且缺乏可解释性 |
算法 | 优势 | 劣势 |
---|---|---|
主动学习算法,如将高斯过程回归(GPR)同随机森林(RF)和支持向量机(SVM)结合,加入人工标注或构建一个获取函数 | 不断迭代提升分类效果,优化效率高 | 需要遍历材料空间以改进模型、需要正向机器 学习模型提供目标属性的预测值和不确定性值 |
生成模型算法,如变分自动编码器(variational autoencoders, VAEs)、生成对抗网络(generative adversarial networks, GANS) | 可以通过学习将输入的参数和性质 快速扩展到未覆盖的材料空间中 | 目前应用在寻找特殊聚合物材料的时间较短, 需要大量后续工作和研究进行验证 |
全局优化算法,如遗传算法(genetic algorithm, GA)、粒子群优化算法(particle swarm optimization, PSO) | 不需要前置数据库和训练好的机器 学习模型 | 有可能只得到局部优化结果 |
表4 用于反求设计的重要机器学习方法、全局优化算法及其优缺点[4,6]
Table 4 A list of important machine learning (ML) and global optimization methods in inverse design and their advantages and disadvantages[4,6]
算法 | 优势 | 劣势 |
---|---|---|
主动学习算法,如将高斯过程回归(GPR)同随机森林(RF)和支持向量机(SVM)结合,加入人工标注或构建一个获取函数 | 不断迭代提升分类效果,优化效率高 | 需要遍历材料空间以改进模型、需要正向机器 学习模型提供目标属性的预测值和不确定性值 |
生成模型算法,如变分自动编码器(variational autoencoders, VAEs)、生成对抗网络(generative adversarial networks, GANS) | 可以通过学习将输入的参数和性质 快速扩展到未覆盖的材料空间中 | 目前应用在寻找特殊聚合物材料的时间较短, 需要大量后续工作和研究进行验证 |
全局优化算法,如遗传算法(genetic algorithm, GA)、粒子群优化算法(particle swarm optimization, PSO) | 不需要前置数据库和训练好的机器 学习模型 | 有可能只得到局部优化结果 |
图5 用机器学习评估的逆向策略来设计具有高玻璃化转变温度(Tg)和带隙(Eg)的聚合物[64]
Fig.5 Machine learning evaluated inverse strategy to design polymers with both high glass transition temperature(Tg)and bandgap(Eg)[64]
图7 基于强化学习和反求设计的聚合物特定分子量分布合成[76-77]
Fig.7 Schematic of the reinforcement learning and inverse design strategy of MWD in the controlled polymerization[76-77]
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[8] | 阳庆元, 刘大欢, 仲崇立. 金属-有机骨架材料的计算化学研究[J]. 化工学报, 2009, 60(4): 805-819. |
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摘要 802
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