Duan Chenru, Nandy Aditya, Terrones Gianmarco G, Kastner David W, 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.
JACS Au. 2022 Dec 1;3(2):391-401. doi: 10.1021/jacsau.2c00547. eCollection 2023 Feb 27.
Transition-metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and nontoxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have well-defined ground states and optimal target absorption energies in the visible region. Machine learning (ML) accelerated discovery could overcome such challenges by enabling the screening of a larger space but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To address this limitation, we search for consensus in predictions among 23 density functional approximations across multiple rungs of "Jacob's ladder". To accelerate the discovery of complexes with absorption energies in the visible region while minimizing the effect of low-lying excited states, we use two-dimensional (2D)efficient global optimization to sample candidate low-spin chromophores from multimillion complex spaces. Despite the scarcity (i.e., ∼0.01%) of potential chromophores in this large chemical space, we identify candidates with high likelihood (i.e., >10%) of computational validation as the ML models improve during active learning, representing a 1000-fold acceleration in discovery. Absorption spectra of promising chromophores from time-dependent density functional theory verify that 2/3 of candidates have the desired excited-state properties. The observation that constituent ligands from our leads have demonstrated interesting optical properties in the literature exemplifies the effectiveness of our construction of a realistic design space and active learning approach.
含有储量丰富的过渡金属的过渡金属发色团因其在照明和无毒生物成像方面的应用而成为重要的设计目标，但它们的设计面临挑战，因为同时具有明确基态和可见光区域最佳目标吸收能量的配合物非常稀少。机器学习（ML）加速发现可以通过筛选更大的空间来克服此类挑战，但受到ML模型训练中所使用数据的保真度的限制，这些数据通常来自单一的近似密度泛函。为了解决这一限制，我们在“雅各布天梯”的多个层级上的23种密度泛函近似的预测中寻找共识。为了加速发现具有可见光区域吸收能量的配合物，同时最小化低激发态的影响，我们使用二维（2D）高效全局优化从数百万个配合物空间中对候选低自旋发色团进行采样。尽管在这个庞大的化学空间中潜在发色团稀少（即约0.01%），但随着ML模型在主动学习过程中得到改进，我们识别出计算验证可能性高（即>10%）的候选物，这意味着发现速度提高了1000倍。来自含时密度泛函理论的有前景发色团的吸收光谱证实，2/3的候选物具有所需的激发态性质。我们的先导物中的组成配体在文献中已表现出有趣的光学性质，这一观察例证了我们构建现实设计空间和主动学习方法的有效性。
