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Energsoft Clean Energy Lab Visit

The process of taking a clean energy research discovery and making a prototype, then rigorously testing and refining it for market readiness, requires equipment and expertise that is expensive to acquire, and rarely available when and where you need it.  


Energsoft software and battery scientist teams are pushing the envelope on batteries that can store much more energy than current lithium-ion cells with help from Washington Clean Energy Testbeds. 

2016 Paris treaty - securing sustainable energy future

Additional Information

 Achieving the 2016 Paris treaty goal of preventive global warming below 2 °C and securing sustainable energy future require ingredients innovations in renewable energy skills. It all starts with battery and renewable energy. And unless these challenges are addressed, the insufficiency of resources, engineering expertise, time to market limitations, and the inability to professionally analyze data to help improve performance and range of electric vehicle or utility batteries could have a substantial impact on the growth of the market and broader adoption of electric vehicles and renewable sources. 


While the window of opportunity is finishing, meeting these goals necessitate deploying new research concepts and strategies to accelerate materials finding. Recent advancements in machine learning have offered the science and engineering community with a flexible and rapid prediction framework, making a tremendous impact. 


In the lab visit, Energsoft Inc. summarize the recent progress in machine learning approaches for developing renewable energy materials and testing them. We summarize applications of machine learning methods for the theoretical approaches in the key renewable energy technologies, including catalysis, battery, solar cell, and crystal discovery. Energsoft also analyze notable applications resulting in the significant development and discuss critical gaps to further accelerate materials discovery. 

Energy Storage Labaratory
Energy Storage Labaratory

Energsoft LABORATORY Presentation


Batteries are critical for our clean energy future and PREDICTIVE ANALYTICS IS THE WAY TO IMPROVE