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# System prepended metadata

title: ML-DFT Project at CASUS

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id: 202008261735
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# ML-DFT Project at CASUS
## Notes from last meeting
### Project 1
- DFT and Warm Dense Matter
- Replace KS equations with ML models
- Encode ionic structure (SNAP, ref below)
- Feed forward neural nets, LDOS

### Project 2
- Physics Informed NNs
- To accelerate KS equation
- Specifically time dependent KS
## Literature Review
### DFT
- The ABC of DFT - K. Burke [https://dft.uci.edu/doc/g1.pdf](https://dft.uci.edu/doc/g1.pdf) (check references too)
- Sherrill Group Notes [http://vergil.chemistry.gatech.edu/notes/](http://vergil.chemistry.gatech.edu/notes/)
- Density Functional Theory - D. Sholl, J. Steckel [https://www.onlinelibrary.wiley.com/doi/abs/10.1002/anie.200905551](https://www.onlinelibrary.wiley.com/doi/abs/10.1002/anie.200905551)
- Time-dependent density functional theory - Marques M.A.L, Gross E.K.U - [link](https://www.annualreviews.org/doi/pdf/10.1146/annurev.physchem.55.091602.094449)
### ML
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations - [https://www.sciencedirect.com/science/article/pii/S0021999118307125](link)
- Neural-net-induced Gaussian process regression for function approximation and PDE solution - [https://www.sciencedirect.com/science/article/pii/S0021999119301032](link)
- A Discussion on Solving Partial Differential Equations using Neural Networks - [https://arxiv.org/abs/1904.07200](link)
### WDM
- Plasma and Warm Dense Matter Studies (LCLS) - [link](https://www-ssrl.slac.stanford.edu/lcls/talks/rlee_wdm_100400.pdf)
- Frontiers and Challenges in Warm Dense Matter (collection of papers) - [link](https://www.springer.com/gp/book/9783319049113)
- Fast and Universal Kohn Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma - [link](https://arxiv.org/abs/2004.02818)
- Ab initio simulation of warm dense matter - [link](https://arxiv.org/abs/1912.09884)
- Efficient formalism for warm dense matter simulations - [link](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.92.161113)
### ML-DFT
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials - [link](https://www.sciencedirect.com/science/article/pii/S0021999114008353)
- Efficient prediction of 3D electron densities using machine learning - [link](https://arxiv.org/abs/1811.06255)
- Bypassing the Kohn-Sham equations with machine learning - [link](https://www.nature.com/articles/s41467-017-00839-3)
- Understanding machine‐learned density functionals - [link](https://onlinelibrary.wiley.com/doi/full/10.1002/qua.25040)
- Finding Density Functionals with Machine Learning - [link](https://dft.uci.edu/pubs/SRHM12.pdf)
- Deep Learning and Density Functional Theory - [link](https://arxiv.org/pdf/1811.08928.pdf)
- Electronic structure from conditional probabilities - [link](https://arxiv.org/abs/2007.01890)
- Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics - [link](https://arxiv.org/abs/1909.00231)
- Completing density functional theory by machine learning hidden messages from molecules - [link](https://www.nature.com/articles/s41524-020-0310-0)
### Group Papers
- Effective Static Approximation: A Fast and Reliable Tool for Warm Dense Matter Theory - [link](https://arxiv.org/abs/2008.02165)
### Misc
- Studying PDEs from Mathematical Methods ..., Arfken Weber Harris
- Look at NeuralPDE Julia package [link](https://github.com/SciML/NeuralPDE.jl)