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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Solid state hydrogen storage: Decoding the path through machine learning
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In this work we have developed HEART (HydrogEn storAge propeRty predicTor), a machine learning framework to discover new metal alloys for efficient hydrogen storage. Our framework includes two ML models that predict the HYdrogen Storage capaciTy as function of temperature (HYST) and the enTHalpy of hydride fORmation (THOR) of multi-component metal alloys. These models helps us to explore millions of possibilities and identify promising materials for hydrogen storage, accelerating the path towards a sustainable future. hashtag#renewableenergy hashtag#hydrogeneconomy hashtag#hydrogenstorage hashtag#machinelearning <!– Headings are cool ======
Blog Post number 4
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Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Solid state hydrogen storage: Decoding the path through machine learning
Published:
In this work we have developed HEART (HydrogEn storAge propeRty predicTor), a machine learning framework to discover new metal alloys for efficient hydrogen storage. Our framework includes two ML models that predict the HYdrogen Storage capaciTy as function of temperature (HYST) and the enTHalpy of hydride fORmation (THOR) of multi-component metal alloys. These models helps us to explore millions of possibilities and identify promising materials for hydrogen storage, accelerating the path towards a sustainable future. #renewableenergy #hydrogeneconomy #hydrogenstorage #machinelearning .png)
MH-PCTpro: A Machine learning model for rapid prediction of Pressure-Composition-Temperature (PCT) isotherms
The Pressure-Composition- Temperature (PCT) analysis is an experimental procedure employed to assess the suitability of an alloy for hydrogen storage. PCT isotherms provide crucial information, like reversible storage capacity, hydrogen pressure for phase transition, and the plateau pressure. Unfortunately, PCT analysis of the materials is resource-intensive and time-consuming as it involves series of measurements to represents the relationship between hydrogen pressure, concentration and temperature of a sample at equilibrium. Hence, limiting the number of compositions that can be investigated. In light of this, we have built a machine learning model, Metal Hydride’s PCT isotherm predictior (MH-PCTpro) for multicomponent metal alloys. The model is trained on diverse family of metal alloy’s PCTs data and the feature set includes easily calculable elements’ periodic table properties, hydriding properties, and experimental parameters. The comprehensive feature set equips PCTpro to predict the PCT isotherms for any metal alloy based on its composition, hydrogen pressure, and temperature. The model is validated across diverse alloy families, agreeing with experimental results. The model also predicts temperature-dependent variations in plateau pressure, enabling the mapping of predicted PCTs onto Van’t Hoff plots to determine enthalpy and entropy of hydride formation. 
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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talks
Talk 1 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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