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Aakash Naik

@naikaakash.bsky.social
35 followers 38 following 2 posts

Ph.D Student at @BAMResearch @MolecularXtal research group Affiliated to @UniJena

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Reposted by Aakash Naik
Janine George @molecularxtal.bsky.social · 15/08/2025
Interested in predicting magnetism in transition metal compounds? We have written a paper on how to use exchange heuristics in such models. We also show limits of current theoretical approaches. Please find our preprint here. doi.org/10.26434/che... #compchemsky
doi.org
Can simple exchange heuristics guide us in predicting magnetic properties of solids?
A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this h...
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Aakash Naik @naikaakash.bsky.social · 09/04/2025
Looking forward to presenting my PhD research at #S25MRS tomorrow! Excited to share my findings and engage in constructive discussions with the community
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Reposted by Aakash Naik
Janine George @molecularxtal.bsky.social · 21/03/2025
CECAM school on automated ab initio calculations came to an end. Nearly all teaching material including videos of our atomate2 school is already or will be online: www.cecam.org/workshop-det... #compchem @virtualatoms.bsky.social @naikaakash.bsky.social and many more not on here 😀
cecam.org
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Reposted by Aakash Naik
Janine George @molecularxtal.bsky.social · 22/01/2025
🤖 Interested in automated DFT or ab initio calculations for crystals or molecules? atomate2 could be your package! doi.org/10.26434/che... #compchem
doi.org
Atomate2: Modular workflows for materials science
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of...
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Reposted by Aakash Naik
Volker Deringer @vlderinger.bsky.social · 07/01/2025
Meet autoplex – our approach to automated ML potential fitting, built jointly with @molecularxtal.bsky.social & team in Berlin! In this preprint, we focus on exploring structures and training potential models "from scratch" with the help of automated workflows: arxiv.org/abs/2412.16736
arxiv.org
An automated framework for exploring and learning potential-energy surfaces
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interato...
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Reposted by Aakash Naik
Janine George @molecularxtal.bsky.social · 20/12/2024
New paper on "Chemical ordering and magnetism in face-centered cubic CrCoNi" alloy together with Sheuly Ghosh, Jörg Neugebauer and Fritz Körmann. Katharina Ueltzen from our group used COHP-based bonding analysis to explain the magnetic ordering in the alloy 🥳 www.nature.com/articles/s41...
nature.com
Chemical ordering and magnetism in face-centered cubic CrCoNi alloy - npj Computational Materials
npj Computational Materials - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy
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