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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…
Autonomous decision making for solid-state synthesis of inorganic materials
Nathan J. Szymanski, Pragnay Nevatia, Christopher J. Bartel +2
To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target…
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
Bowen Deng, Peichen Zhong, KyuJung Jun +4
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force fi…
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…