{"title":"Towards self-driving/autonomous material discovery lab","authors":"U Deva Priyakumar","doi":"10.1007/s12039-025-02431-5","DOIUrl":null,"url":null,"abstract":"<div><p>Self-driving or autonomous labs are platforms that integrate artificial intelligence (AI)/machine learning (ML), robotics and high-throughput experiments, and are capable of designing, synthesizing, testing and optimizing materials for specific purposes with minimal human intervention. Crucial components are algorithms and methods that are capable of generating new materials with desired properties. A recent study by Zeni <i>et al</i>. has reported a material generative model, MatterGen that is fine-tuned to propose new materials conditioned on user-specific mechanical, electronic and magnetic properties.</p><h3>Graphical abstract</h3><p>Fully autonomous chemistry laboratories bring together advanced generative AI algorithms, robotic systems and high-throughput experiments that can computationally design, and experimentally synthesize and characterize molecules/materials. In this news story, we discuss the importance of methods such as recently proposed MatterGen in making self-driving laboratories a reality.</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":616,"journal":{"name":"Journal of Chemical Sciences","volume":"137 4","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Sciences","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s12039-025-02431-5","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Self-driving or autonomous labs are platforms that integrate artificial intelligence (AI)/machine learning (ML), robotics and high-throughput experiments, and are capable of designing, synthesizing, testing and optimizing materials for specific purposes with minimal human intervention. Crucial components are algorithms and methods that are capable of generating new materials with desired properties. A recent study by Zeni et al. has reported a material generative model, MatterGen that is fine-tuned to propose new materials conditioned on user-specific mechanical, electronic and magnetic properties.
Graphical abstract
Fully autonomous chemistry laboratories bring together advanced generative AI algorithms, robotic systems and high-throughput experiments that can computationally design, and experimentally synthesize and characterize molecules/materials. In this news story, we discuss the importance of methods such as recently proposed MatterGen in making self-driving laboratories a reality.
期刊介绍：
Journal of Chemical Sciences is a monthly journal published by the Indian Academy of Sciences. It formed part of the original Proceedings of the Indian Academy of Sciences – Part A, started by the Nobel Laureate Prof C V Raman in 1934, that was split in 1978 into three separate journals. It was renamed as Journal of Chemical Sciences in 2004. The journal publishes original research articles and rapid communications, covering all areas of chemical sciences. A significant feature of the journal is its special issues, brought out from time to time, devoted to conference symposia/proceedings in frontier areas of the subject, held not only in India but also in other countries.
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