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Academic Publication Data Generation for Machine Learning Interatomic Potentials and Beyond
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Connecting this academic literature to real-world market discussions and products.
Scalable emulation of protein equilibrium ensembles with generative deep learning
Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system t...
MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data
Abstract We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction te...
Efficient and Accurate Machine Learning of Thermophysical Properties from Small Data
Thermophysical properties of working fluids play a crucial role in advanced propulsion systems, directly influencing heat transfer and fluid flow, and phase behavior under extreme conditions. Accur...
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
No description provided.
Fast interlayer exciton drift driven by lattice reconstruction in a van der Waals heterobilayer
Time-resolved measurements of interlayer exciton transport in van der Waals heterobilayers show that lattice reconstruction generates potential energy gradients, driving rapid exciton drift towards...
Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'Data Generation for Machine Learning Interatomic Potentials and Beyond'?
This literature focuses on:
Are there open-source GitHub repositories related to Data Generation for Machine Learning Interatomic Potentials and Beyond?
Yes, open-source projects like PKU-YuanGroup/Helios (Helios: Real Real-Time Long Video Generation Model) are actively building upon these concepts.
Which startups are commercializing the technology behind Data Generation for Machine Learning Interatomic Potentials and Beyond?
Products like Superset are bringing this to market. Their focus is: Run an army of Claude Code, Codex, etc. on your machine.
What other academic literature is closely related to 'Data Generation for Machine Learning Interatomic Potentials and Beyond'?
Yes, highly correlated activity was mapped. An entry titled 'Scalable emulation of protein equilibrium ensembles with generative deep learning' discusses this: Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challe...
Are there commercial applications of 'Data Generation for Machine Learning Interatomic Potentials and Beyond' in market news publications?
Yes, highly correlated activity was mapped. An entry titled 'Fast interlayer exciton drift driven by lattice reconstruction in a van der Waals heterobilayer' discusses this: Time-resolved measurements of interlayer exciton transport in van der Waals heterobilayers show that lattice reconstruction generates potential ene...
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Commercial Realization
Startups and Open Source tools heavily associated with the concepts explored in this paper.
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GitHubPKU-YuanGroup/Helios
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GitHubTHU-MAIC/OpenMAIC
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Product HuntSuperset
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Product HuntNano Banana 2