Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K.
J Am Chem Soc. 2022 Oct 19;144(41):18730-18743. doi: 10.1021/jacs.2c06833. Epub 2022 Oct 7.
Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.
急需新型功能材料来帮助应对人类面临的重大全球挑战,如气候变化和资源短缺。然而,传统的实验材料发现过程缓慢,而且可用的材料空间太大,仅凭直觉引导的实验无法有效地探索。大多数实验材料发现计划都必然侧重于探索已知材料的局部空间,因此我们并没有充分利用巨大的潜在材料空间,而这些空间可能存在具有独特性质的更多新型材料。得益于开源软件和数据库以及计算机硬件的改进,计算有潜力极大地加速材料的合理开发,但它往往仅用于推理实验观察结果。因此,计算的真正预测能力(即理论指导实验)并没有得到充分利用。在这里,我们讨论了成功实施计算驱动的材料发现工作流程所面临的挑战,然后重点介绍了该领域的进展,特别强调了实现新型材料所面临的挑战。