Deep learning has proven to yield fast and accurate predictions of
quantum-chemical properties to accelerate the discovery of novel molecules and
materials. As an exhaustive exploration of the vast chemical space is still
infeasible, we require generative models that guide our search towards systems
with desired properties. While graph-based models have previously been
proposed, they are restricted by a lack of spatial information such that they
are unable to recognize spatial isomerism and non-bonded interactions. Here, we
introduce a generative neural network for 3d point sets that respects the
rotational invariance of the targeted structures. We apply it to the generation
of molecules and demonstrate its ability to approximate the distribution of
equilibrium structures using spatial metrics as well as established measures
from chemoinformatics. As our model is able to capture the complex relationship
between 3d geometry and electronic properties, we bias the distribution of the
generator towards molecules with a small HOMO-LUMO gap - an important property
for the design of organic solar cells.

利用深度学习的生成模型对分子和材料进行快速的预测，同时引入了一种能够识别非键合作用和立体异构体，并且能够偏向于小 HOMO-LUMO 间隙分子生成的生成式神经网络。

用于有针对性分子发现的对称适应的 3D 点集生成

Symmetry-adapted generation of 3d point sets for the targeted discovery  of molecules

The optimization of composition and processing to obtain materials that
exhibit desirable characteristics has historically relied on a combination of
scientist intuition, trial and error, and luck. We propose a methodology that
can accelerate this process by fitting data-driven models to experimental data
as it is collected to suggest which experiment should be performed next. This
methodology can guide the scientist to test the most promising candidates
earlier, and can supplement scientific intuition and knowledge with data-driven
insights. A key strength of the proposed framework is that it scales to
high-dimensional parameter spaces, as are typical in materials discovery
applications. Importantly, the data-driven models incorporate uncertainty
analysis, so that new experiments are proposed based on a combination of
exploring high-uncertainty candidates and exploiting high-performing regions of
parameter space. Over four materials science test cases, our methodology led to
the optimal candidate being found with three times fewer required measurements
than random guessing on average.

提出了一种利用数据驱动模型加速材料发现优化过程的方法，该方法可以指导科学家先测试最有前途的候选者，并结合不确定性分析为探索高不确定度的候选者和利用参数空间高性能区域提供建议。该方法在材料科学领域的四个测试案例表明，比随机猜测平均少三倍的实验次数就找到了最佳候选方案。

使用数据驱动的实验设计和好校准的不确定性估计进行高维材料和工艺优化

High-Dimensional Materials and Process Optimization using Data-driven  Experimental Design with Well-Calibrated Uncertainty Estimates

It is essential to know the arrangement of the atoms in a material in order
to compute and understand its properties. Searching for stable structures of
materials using first-principles electronic structure methods, such as density
functional theory (DFT), is a rapidly growing field. Here we describe our
simple, elegant and powerful approach to searching for structures with DFT
which we call ab initio random structure searching (AIRSS). Applications to
discovering structures of solids, point defects, surfaces, and clusters are
reviewed. New results for iron clusters on graphene, silicon clusters,
polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.

本文介绍了一种基于密度泛函理论的方法 ——ab initio 随机结构搜索（AIRSS），其可应用于发现固体、点缺陷、表面和团簇的结构。并提出了关于石墨烯上的铁团簇、硅团簇、聚合氮、富氢锂氢化物和硼等方面的新成果。