Prof Ong gave a talk on bridging computational and experimental predictions in materials machine learning models at the CNLS Virtual Workshop Machine Learning in Chemical and Materials Sciences held on May 12 2021. This talk discusses the sources of ML prediction errors, namely model errors and data errors, and demonstrate how these errors can be mitigated using appropriate techniques. For example, multi-fidelity/multi-task models can help small data models learn from larger, less accurate data models, while choosing an appropriate DFT functional for computing energies and forces for ML interatomic potentials can significantly improve the agreement with experimental measurements. You can jump to the relevant chapter of interest below!
Dr Chi Chen gave a talk at the Global XAS Journal Club on the Materials Virtual Lab’s efforts at constructing large X-ray absorption spectra databases using high-throughput computation and the development of machine learning models that can supercharge the interpretation of such spectra.
Prof Ong gave a talk on “Discovering New Materials in a Fraction of the Time with Graph Networks” at the NVIDIA GTC 2021 conference. This talk discusses our recent work on using GPU-trained multi-fidelity graph networks, together with Bayseian optimization techniques, to discover novel materials.
Chi Chen gave a talk at nanoHub’s Hands-on Data Science and Machine Learning Training Series on how to develop MatErials Graph Network (MEGNet) models for predicting various materials properties from crystal structure. He also demonstrates how the MEGNet framework can be adapted to work with multi-fidelity data sources to improve predictions on high-value small datasets (e.g., experimental data). Extensive examples are shown using Jupyter notebooks. The video is available on the Materials Virtual Lab Youtube Channel. The megnet package used extensively in these tutorials can be found on Github.
Yunxing gave a talk at NanoHUB’s Hands-on Data Science and Machine Learning Training Series today on how to conveniently develop machine learning interatomic potentials (ML-IAPs) using the Materials Machine Learning (maml) library. ML-IAPs describe the potential energy surface using local environment descriptors and has been demonstrated to be able to achieve near-DFT accuracy with linear scaling with respect to the number of atoms. The recording of this talk is now available on the Materials Virtual Lab’s Youtube channel. To find out more about the maml package, check out our Github repository. You can also read Yunxing’s excellent paper benchmarking the performance and cost of various ML-IAPs to learn more.
Prof Ong gave a webinar talk on the AI Revolution in Materials Science for the Singapore Agency of Science Technology and Research (A*STAR). In this talk, he discussed the big challenges in materials science where AI can make a huge impact towards addressing as well as outstanding challenges and opportunities to bringing forth the AI revolution to the materials domain.