63 citations · 105 across the 3 of their papers we have counts for
5 papers · 1 filter
Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials
Matthew J. McDermott, Brennan C. McBride, Corlyn Regier +11
Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedure…
Selective formation of metastable polymorphs in solid-state synthesis
Yan Zeng, Nathan J. Szymanski, Tanjin He +6
Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a l…
Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry
Santosh Adhikari, Christopher J. Bartel, Christopher Sutton
This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 10…
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Tanjin He, Haoyan Huo, Christopher J. Bartel +3
Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challeng…
Testing the rSCAN density functional for the thermodynamic stability of solids with and without a van der Waals correction
Manish Kothakonda, Aaron D. Kaplan, Eric B. Isaacs +6
A central aim of materials discovery is an accurate and numerically reliable description of thermodynamic properties, such as the enthalpies of formation and decomposition. The r$^…