5 research outputs found
Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
We present a benchmark test suite and an automated machine learning procedure
for evaluating supervised machine learning (ML) models for predicting
properties of inorganic bulk materials. The test suite, Matbench, is a set of
13 ML tasks that range in size from 312 to 132k samples and contain data from
10 density functional theory-derived and experimental sources. Tasks include
predicting optical, thermal, electronic, thermodynamic, tensile, and elastic
properties given a materials composition and/or crystal structure. The
reference algorithm, Automatminer, is a highly-extensible, fully-automated ML
pipeline for predicting materials properties from materials primitives (such as
composition and crystal structure) without user intervention or hyperparameter
tuning. We test Automatminer on the Matbench test suite and compare its
predictive power with state-of-the-art crystal graph neural networks and a
traditional descriptor-based Random Forest model. We find Automatminer achieves
the best performance on 8 of 13 tasks in the benchmark. We also show our test
suite is capable of exposing predictive advantages of each algorithm - namely,
that crystal graph methods appear to outperform traditional machine learning
methods given ~10^4 or greater data points. The pre-processed, ready-to-use
Matbench tasks and the Automatminer source code are open source and available
online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating
new materials ML algorithms on the MatBench benchmark and comparing them
against the latest version of Automatminer.Comment: Main text, supplemental inf
Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</p