Month: January 2020

Our critical review of the application of machine learning (ML) in Energy Materials led by Chi Chen is now out in Advanced Energy Materials. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. In this work, we provide a conceptual framework for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials, including rechargeable alkali-ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors. We conclude the work with our perspectives on major challenges and opportunities in this exciting field. Check out the work here.

Our work on “Performance and Cost Assessment of Machine Learning Interatomic Potentials (ML-IAPs)” has been published in the Journal of Physical Chemistry A! Co-authored with the developers of four leading ML-IAPs, this work provides a rigorous assessment of ML-IAPs across several metrics – accuracy in energies and forces, materials properties and training and computing cost. This assessment was carried out using a diverse data set – bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) – generated using high-throughput density functional theory (DFT) calculations. To facilitate the reuse and reproduction of our results, the code, data and optimized ML models in this work are published open-source on our mlearn Github repo. The code includes high-level Python interfaces for ML-IAPs development as well as LAMMPS material properties calculators. Check out the publication at this link.

We are pleased to announce the release of the Grain Boundary Database (GBDB) together with the associated publication in Acta Materialia! The GBDB is the largest database of DFT-computed grain boundary properties to date, encompassing 327 GBs of 58 elemental metals. To construct the GBDB, we developed a novel scaled-structural template approach for GB calculations, which reduces the computational cost of converging GB structures by a factor of ~ 3–6. The grain boundary energies and work of separation have been rigorously validated against previous experimental and computational data. You can check out the GBDB at Crystalium@Materials Virtual Lab or the Materials Project.