A. Natarajan

activity

20162026

most citedMachine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

113 citations · 175 across the 12 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cond-mat.mtrl-sci2019★ 27 cited

Machine learning the DFT potential energy surface for inorganic halide perovskite CsPbBr $_{3}$

John C. Thomas, Jonathon S. Bechtel, Anirudh Raju Natarajan +1

Structural phase transitions as a function of temperature dictate the structure--functionality relationships in many technologically important materials. Harmonic Hamiltonians have…

cond-mat.mtrl-sci2019★ 113 cited

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

G. H. Teichert, A. R. Natarajan, A. Van der Ven +1

The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this wo…

Machine learning the DFT potential energy surface for inorganic halide perovskite CsPbBr3​

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

Machine learning the DFT potential energy surface for inorganic halide perovskite CsPbBr $_{3}$