Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
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DOI：
10.1021/acs.inorgchem.9b00109
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发表时间：
2019-08-19
影响因子：
4.6
通讯作者：
Kulik, Heather J.
中科院分区：
文献类型：
--
作者：
Janet, Jon Paul;Liu, Fang;Kulik, Heather J.
最近在计算能力和算法方面的革命性进展使计算化学成为发现和设计新分子和材料的核心。第一性原理模拟越来越准确，适用于具有高通量计算筛选所需速度的大型系统。尽管取得了这些进展，但与巨大的化学空间相关的组合挑战意味着，要加速化学发现，需要的不仅仅是快速和准确的计算工具。在过渡金属化学和催化领域，出现了独特的挑战。具有良好局域化d或f电子的元素所支持的可变自旋、氧化态和配位环境为定制催化或功能(例如，磁性)材料的性质提供了极大的机会，但也为任何设计策略增加了不确定层。我们概述了实现计算驱动的无机化学加速发现的五个关键任务：(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)快于快速的性质预测方法，(Iv)快速化学空间遍历的地图，以及(V)揭示千化合物尺度上设计规则的手段。通过开壳过渡金属化学的案例研究，我们描述了这些领域中方法学和软件的进步如何带来新的化学见解。最后，我们对这一过程中的下一步进行了展望，以实现使用计算化学在无机化学中的完全自主发现。
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.