Geometric Density Functional Theory

Geometric Density Functional Theory is an attempt to directly compute the universal energy functional and its (functional) derivatives from the solution of an optimization problem in which the electronic density is kept constant.  The process can be described as "finding the ground-state of an electron gas of prescribed density". In this approach the target density is established and maintained through a geometric deformation of the original coordinates.

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Foundational Machine Learning Potentials - Challenges and Opportunities

Graph neural network interatomic potentials have emerged as powerful tools for accelerating materials simulation and property prediction.

The latest generation of models approach about ab initio accuracy while maintaining linear scaling of compute cost with system size, promising high quality molecular dynamics at unprecedented time and length scales.

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