colabfit/JARVIS_TinNet_OH

JARVIS TinNet OH数据集是JARVIS数据库的一个子集，用于训练机器学习模型，以预测过渡金属表面的化学活性，进而帮助设计催化剂。该数据集包含了748种不同的分子配置，涵盖了13464个原子，包括铝、氢、氧、铑、铱、钯、铬、铜、铂、铑、铼、钪、锇、钽、钼、镍、钨、钒、金、铊、铁、锌、锡、银、铅、钴、钛、铌、钇、锰、锆、镧、镓、铋、铟、铪、镉等多种元素。数据集中的属性包括吸附能量、原子力和Cauchy应力等。

The JARVIS TinNet OH dataset is a subset of the JARVIS database, used for training machine learning models to predict the chemical reactivity of transition-metal surfaces for catalyst design. This dataset includes 748 unique molecular configurations and encompasses 13,464 atoms, covering elements such as Al, H, O, Ru, Ir, Pd, Cr, Cu, Pt, Rh, Re, Sc, Os, Ta, Mo, Ni, W, V, Au, Tl, Fe, Zn, Sn, Ag, Pb, Co, Ti, Nb, Y, Mn, Zr, La, Ga, Bi, In, Hf, Cd. Properties included in the dataset are adsorption energy, atomic forces, and Cauchy stress.