We are excited to announce an update to the Energy-GNoME platform: enhanced machine learning models for both the Thermoelectric and Perovskite databases! These improvements mark a significant step forward in the predictive power and practical utility of our platform, enabling faster and more accurate discovery of high-performance materials.
We introduced a new feature that enhances the Energy-GNoME database's usability: dedicated web pages for individual materials! This update brings a significant step forward in how researchers and enthusiasts can explore materials, leveraging advanced visualization tools and property insights and enabling people who are not computational material experts to fully explore the database, alongside the dashboard.
We are thrilled to introduce the dedicated Cathode Database within the Energy-GNoME platform. Cathode materials are at the heart of modern battery technologies, playing a critical role in enabling efficient and sustainable energy storage solutions. From lithium-ion to emerging multivalent systems, the search for optimal cathode materials is key to advancing the next generation of energy storage devices.
We are excited to announce the dedicated Perovskite Database within the Energy-GNoME platform. Perovskites, with their extraordinary versatility and wide range of applications, represent a cornerstone of modern energy materials research. From solar cells to optoelectronics, perovskites are transforming the way we think about energy conversion and efficiency.
We are excited to announce the dedicated Thermoelectric Database within the Energy-GNoME platform. Thermoelectric materials, renowned for their ability to convert heat into electricity and vice versa, hold immense potential for advancing sustainable energy technologies. From waste heat recovery to solid-state cooling, thermoelectric materials are reshaping our understanding of energy efficiency.
We are thrilled to announce the first release of the Energy-GNoME database, version 0.0.2. This milestone represents the culmination of extensive research and development by our team, aimed at accelerating energy material discoveries through advanced machine learning (ML) methodologies.