Institute of Ion Physics and Applied Physics, University of Innsbruck, 6020 Innsbruck, Austria.
Department of Inorganic Chemistry, Tashkent Chemical Technological Institute, 100011 Tashkent, Uzbekistan.
J Chem Inf Model. 2024 Aug 12;64(15):5762-5770. doi: 10.1021/acs.jcim.3c01990. Epub 2024 Jul 15.
We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry.
我们提出了一种新的通用机器学习模型，能够预测各种晶体性质，包括费米能级能量和带隙，以及光谱性质，如电子态密度。该模型基于原子表示，使其能够有效地捕获晶体中每个原子及其周围环境的复杂信息。对于带隙的准确性，我们的模型超过了之前发表的结果。通过设计，我们的模型不受限于此处讨论的电子性质，而是可以扩展以适应各种化学描述符。它的优点是：（a）计算要求低，使其成为材料高通量筛选的有效工具；（b）其架构的简单性和灵活性，便于实施和解释，特别是对于计算化学领域的研究人员。
