Brennon Shanks (@brennonlshanks) 's Twitter Profile
Brennon Shanks

@brennonlshanks

Researcher on liquid state materials @ University of Utah, Department of Chemical Engineering

ID: 1620308165614239750

linkhttps://bshanks.netlify.app calendar_today31-01-2023 06:29:18

9 Tweet

7 Followers

38 Following

Machine Learning in Chemistry (@ml_chem) 's Twitter Profile Photo

Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement #machinelearning bit.ly/3FwVUdS

Katherine Steinberg (@kjsteinb) 's Twitter Profile Photo

Excited to share that our paper on the role of SEI in lithium-mediated ammonia synthesis is now out in Nature Energy! nature.com/articles/s4156โ€ฆ

The Journal of Physical Chemistry (@jphyschem) 's Twitter Profile Photo

Analysis of neutron scattering measurements in noble gases reveals possible connections between classical and quantum mechanical effects in many-body systems. Michael Hoepfner Jeffrey Potoff go.acs.org/3G3

Jeffrey Potoff (@jpotoff) 's Twitter Profile Photo

This is a very nice work by Brennon Shanks (linkedin.com/in/brennon-shaโ€ฆ) who is doing some great work with Michael Hoepfner. Happy I could contribute to it.

Machine Learning: Science and Technology (@mlstjournal) 's Twitter Profile Photo

Great new work by @ramav_matsci Sergei Kalinin Maxim Ziatdinov OLCF Physical Sciences UT MSE Tickle College of Engineering - 'Optimizing training trajectories in variational #autoencoders via latent #Bayesian optimization approach' - bit.ly/3HwBwJI #machinelearning #materials #compchem #AI

Great new work by @ramav_matsci <a href="/Sergei_Imaging/">Sergei Kalinin</a> <a href="/MaximZiatdinov/">Maxim Ziatdinov</a> <a href="/OLCFGOV/">OLCF</a> <a href="/ORNL_PhysSci/">Physical Sciences</a> <a href="/UT_MSE/">UT MSE</a> <a href="/UTK_TCE/">Tickle College of Engineering</a> - 'Optimizing training trajectories in variational #autoencoders via latent #Bayesian optimization approach' - bit.ly/3HwBwJI #machinelearning #materials #compchem #AI
Brennon Shanks (@brennonlshanks) 's Twitter Profile Photo

Check out this really great article on using remote sensing data to map tree species distributions in Hawaii! Congrats Megs! mdpi.com/2072-4292/15/1โ€ฆ

Brennon Shanks (@brennonlshanks) 's Twitter Profile Photo

Check out our recently published article on using local Gaussian processes as surrogate models for molecular simulation force field training and design! pubs.acs.org/doi/10.1021/acโ€ฆ

Brennon Shanks (@brennonlshanks) 's Twitter Profile Photo

New insights into interatomic forces are emerging from the intersection of physics and machine learning. Our latest research shows how accurately scattering experiments can predict these forces. #MachineLearning #Physics pubs.acs.org/doi/10.1021/acโ€ฆ

Brennon Shanks (@brennonlshanks) 's Twitter Profile Photo

Probabilistic ML is enabling new ways to connect experimental structure data with molecular interactions. Our latest work shows that interatomic potentials learned from scattering data in liquids behave like quantum Drude oscillators. doi.org/10.1063/5.0260โ€ฆ

Brennon Shanks (@brennonlshanks) 's Twitter Profile Photo

Cationโˆ’ฯ€ interactions in TMA are biologically important but hard to model due to polarization effects. Here we show that charge scaling in MD improves structural predictions in aqueous TMA systems with no added computational cost. pubs.acs.org/doi/10.1021/acโ€ฆ