Search Results for author:
Found 8 papers, 4 papers with code
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important.
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Synthesis prediction is a key accelerator for the rapid design of advanced materials.
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra.
A critical examination of compound stability predictions from machine-learned formation energies
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85, 014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
Materials Science Computational Physics
The role of decomposition reactions in assessing first-principles predictions of solid stability
This analysis shows that the decomposition into elemental forms is rarely the competing reaction that determines compound stability and that approximately two-thirds of decomposition reactions involve no elemental phases.
Materials Science
Physical descriptor for the Gibbs energy of inorganic crystalline solids and prediction of temperature-dependent materials chemistry
Using the resulting predicted thermochemical data, we generate thousands of temperature-dependent phase diagrams to provide insights into the effects of temperature and composition on materials synthesizability and stability and to establish the temperature-dependent scale of metastability for inorganic compounds.
Materials Science
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for photovoltaics, fuel cells, and many other applications.
Materials Science
