Aakash Naik

Further details on the content can be found here : lnkd.in/eBzHBTwQ

My talk will be part of the "Inclusion of domain knowledge and Physical theory in machine learning" symposium on April 9th at 16:30.

Looking forward to presenting my PhD research at #S25MRS tomorrow! Excited to share my findings and engage in constructive discussions with the community

🚨 Introducing graph-pes: a unified framework for building, training and using graph-based machine-learned models of potential energy surfaces! 🚨 #compchem #ML

Thank you to all the amazing participants in this work! It's truly an honor to build all of this with you. 📰arxiv.org/abs/2411.15221

PLEASE RETWEET: Come and spend 3.5 years in our group using DFT and AI/ML to find and characterise new wide band gap oxides for optoelectronics, in our shiny office in our fancy new building! @UoBChemistry @uob_bear tinyurl.com/3ntx7prh Photo credit to @prslaterchem!

@BAMResearch @MolecularXtal @ERC_Research Congratulations 🥳

We're looking for a talented postdoc to join our eScience group! If you have strong skills in machine learning and excellent programming abilities, we want to hear from you. We are a diverse team and offer many challenging projects! bam.de/umantis/EN/197…

Want to use ACE but don’t have sufficient experience with the hyperparameters? XPOT might be a good starting point! Check our latest preprint on hyperparameter optimisation using XPOT. Other interesting works will follow 🙌🏻

Dr. Lucia Casali @casali_lucia (Federal Institute for Materials Research and Testing) @BAMResearch explains how to break the wall of pharmaceutical manufacturing by using innovative mechanochemical processes to synthesize green active pharmaceutical ingredients. @FallingWallsLab

A huge thank you to all the organizers and participants for making this hackathon such a valuable learning experience! 🙌 @HumboldtUni #LLMHackathon2024 #AI4Science #MaterialsScience #Chemistry

🌟 This hackathon was an invaluable experience! Learned new skills and made valuable contacts, and we are excited to continue refining our model and exploring its potential applications!

Excited to share our experience from the "LLM Applications in Materials and Chemistry 2024" hackathon this week! Me and Katharina Ueltzen went into this event as a team from the @MolecularXtal group at @BAMResearch in #Berlin

It's been a rewarding collaborative effort, and I'm grateful to have contributed. This journey has been a valuable learning experience. I'm eager to see how it develops further. If you have any suggestions or further feature requests, feel free to reach out to us!

1st pymatgen release of the year v2024.1.26 now on PyPI! Big thanks to @alexganose for a significant (backwards compatible!) refactor of the VASP input sets (to enable merging atomate2 input sets into pmg). Also in this release: over a dozen bug fixes, ... github.com/materialsproje…

@MolecularXtal Happy new year 🥳