Chowdhury Chandra
Advanced Materials Laboratory, CSIR-Central Leather Research Institute, Sardar Patel Road, Adyar, Chennai, 600020, India.
Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany.
Chemphyschem. 2024 Aug 19;25(16):e202300850. doi: 10.1002/cphc.202300850. Epub 2024 Jul 24.
The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) are crucial for addressing global challenges like climate change, energy security, and environmental degradation. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the complex design spaces of NPMs for gas storage applications. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO using a curated set of surrogate model and acquisition function couples. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, including the conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimisation python library (BO Torch) outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery and addresses urgent needs in energy storage and environmental sustainability. It is to be noted that rather than identifying new MOFs, BO primarily enhances computational efficiency by reducing the reliance on more demanding calculations, such as those involved in Grand Canonical Monte Carlo (GCMC) or Density Functional Theory (DFT).
发现和优化新型纳米多孔材料（NPMs），如金属有机框架（MOFs）和共价有机框架（COFs），对于应对气候变化、能源安全和环境退化等全球挑战至关重要。优化这些材料的传统实验方法既耗时又耗费资源。本研究论文提出了一种使用贝叶斯优化（BO）的策略，以有效地探索用于气体存储应用的NPMs复杂设计空间。对于从19个不同来源获取的MOF数据集，我们使用一组精心策划的代理模型和采集函数对来对BO进行定量评估。在我们的研究中，我们采用机器学习（ML）技术对许多模型进行回归分析。在此之后，我们确定了三个准确率最高的ML模型，随后在我们的研究中选择它们作为代理模型，包括传统的高斯过程（GP）模型。我们发现，以预期改进（EI）作为采集函数但没有贝叶斯优化Python库（BO Torch）中的标准伽马先验的GP模型优于其他代理模型。此外，应该注意的是，虽然在预测目标变量方面表现优异的机器学习模型可能被认为是最佳选择，但它不一定是BO最合适的代理模型。这一观察结果具有重要意义，值得进一步研究。这个综合框架加快了材料发现的步伐，并满足了储能和环境可持续性方面的迫切需求。需要注意的是，BO主要不是识别新的MOF，而是通过减少对更苛刻计算的依赖来提高计算效率，例如那些涉及巨正则蒙特卡罗（GCMC）或密度泛函理论（DFT）的计算。
