activity
20182023
most citedNew Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

1.8k citations · 3.5k across the 17 of their papers we have counts for

collaborators

17 papers

cond-mat.mtrl-sci2023★ 38 cited

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…

cond-mat.mtrl-sci2023★ 2 cited

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…

cond-mat.mtrl-sci2023★ 1 cited

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…

cond-mat.mtrl-sci2023★ 46 cited

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…

cond-mat.mtrl-sci2023★ 19 cited

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…

cond-mat.mtrl-sci2023★ 63 cited

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…