The ongoing energy crisis has underscored the urgent need for energy-efficient materials with high energy utilization efficiency, prompting a surge in research into organic compounds due to their environmental compatibility, cost-effective processing, and versatile modifiability. To address the high experimental costs and time-consuming nature of traditional trial-and-error methods in the discovery of highly functional organic compounds, we apply the 3D transformer-based molecular representation learning algorithm to construct a pre-trained model using 60 million semi-empirically optimized structures of small organic molecules, namely, Org-Mol, which is then fine-tuned with public experimental data to obtain prediction models for various physical properties. Despite the pre-training process relying solely on single molecular coordinates, the fine-tuned models achieves high accuracy (with $R^2$ values for the test set exceeding 0.95). These fine-tuned models are applied in a high-throughput screening process to identify novel immersion coolants among millions of automatically constructed ester molecules, resulting in the experimental validation of two promising candidates. This work not only demonstrates the potential of Org-Mol in predicting bulk properties for organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials.
MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to The Discovery of Organic Flow Battery Electrolytes
我们提出了一个多描述符多内核（MultiDK）的方法，有效的分子发现使用机器学习。我们表明，MultiDK方法提高了分子性质预测的速度和准确性。我们将该方法应用于发现用于水性氧化还原液流电池的电解质分子。使用\emph{multiple-type - as opposed to single-type - descriptors}，可以获得用于机器学习的更多相关特征。遵循“群众智慧”的原则，多类型描述符的组合显著提高了预测性能。此外，MultiDK可以利用分子结构和性能关系之间的不规则性比线性回归方法更好地采用多个内核-一组输入描述符的一个以上的核函数。多个核分别由一组二进制描述符和一组非二进制描述符的Tanimoto相似性函数和线性核组成。使用MultiDK，我们实现了$r^2 = 0.92$的平均性能与一组分子的溶解度预测。我们还扩展了MultiDK来预测pH依赖的溶解度，并将其应用于具有可电离官能团的醌分子的溶解度估计，作为液流电池电解质的强有力的候选者。
High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques
Peking University（北京大学）；Eastern Institute of Technology（东方理工大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）；The University of Toledo（托莱多大学）；WuXi AppTec（药明康德）；Peng Cheng Laboratory（鹏城实验室）；Southern University of Science and Technology（南方科技大学）
Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling
AI for Science Institute（人工智能科学研究院）；DP Technology（DP技术）；College of Chemistry and Molecular Engineering（化学与分子工程学院）；School of Mathematical Sciences（数学科学学院）；Center for Machine Learning Research（机器学习研究中心）
Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
Shanghai Jiao Tong University（上海交通大学）；Institute of Process Engineering, Chinese Academy of Sciences（中国科学院过程工程研究所）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Minhang District Center for Disease Control（闵行区疾病预防控制中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；National Clinical Research Center for Kidney Diseases（国家肾脏疾病临床医学研究中心）
Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches
Department of Materials Design and Innovation, University at Buffalo（布法罗大学材料设计与创新系）；Department of Materials Science and Engineering, National University of Singapore（新加坡国立大学材料科学与工程系）
