Janet Jon Paul, Liu Fang, Nandy Aditya, Duan Chenru, Yang Tzuhsiung, Lin Sean, Kulik Heather J
Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States.
Department of Chemistry , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States.
Inorg Chem. 2019 Aug 19;58(16):10592-10606. doi: 10.1021/acs.inorgchem.9b00109. Epub 2019 Mar 5.
Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.
近年来，计算能力和算法取得了变革性进展，使得计算化学成为新分子和材料发现与设计的核心。第一性原理模拟越来越精确，并且适用于大型系统，具备高通量计算筛选所需的速度。尽管取得了这些进展，但与化学空间的广阔性相关的组合挑战意味着，加速化学发现不仅需要快速且精确的计算工具。在过渡金属化学和催化领域，出现了独特的挑战。具有局域化d或f电子的元素所偏好的可变自旋、氧化态和配位环境，为在催化或功能（如磁性）材料中定制性能提供了巨大机会，但也给任何设计策略增添了层层不确定性。我们概述了在无机化学中实现计算驱动的加速发现的五项关键任务：（i）新化合物的全自动模拟，（ii）预测灵敏度或准确性的知识，（iii）比快速更快的性质预测方法，（iv）用于快速化学空间遍历的图谱，以及（v）一种在千化合物规模上揭示设计规则的方法。通过开壳层过渡金属化学的案例研究，我们描述了这些领域中每个领域的方法和软件进展如何带来新的化学见解。我们以对这一过程下一步的展望作为结尾，即利用计算化学在无机化学中实现完全自主的发现。
