7 citations · 8 across the 2 of their papers we have counts for
4 papers
Differentiable thermodynamic modeling
Pin-Wen Guan
A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemica…
MeltNet: Predicting alloy melting temperature by machine learning
Pin-Wen Guan, Venkatasubramanian Viswanathan
Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a…
Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling
Ying Yuan, Gregory Houchins, Pin-Wen Guan +1
In this work, an assessment of the CALPHAD method trained on only density functional theory (DFT) data is performed for the Li-Si binary system, as a case study. Using a parameter…
Uncertainty Quantification of DFT-predicted Finite Temperature Thermodynamic Properties within the Debye Model
Pinwen Guan, Gregory Houchins, Venkatasubramanian Viswanathan
Finite-temperature effects can be included by calculating the vibrations properties and this can greatly improve the fidelity of computational screening. An important challenge for…