Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape

By Kamal Choudhary, Brian L. DeCost, Francesca Tavazza. 2018

144 citations recorded by the source corpus.

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow differentiating between structural prototypes, which is not possible using the commonly used chemical-only descriptors. Specifically, we demonstrate that the combination of pairwise radial, nearest neighbor, bond-angle, dihedral-angle and core-charge distributions plays an important role in predicting formation energies, bandgaps, static refractive indices, magnetic properties, and modulus of elasticity for three-dimensional (3D) materials as well as exfoliation energies of two-dimensional (2D) layered materials. The training data consists of 24549 bulk and 616 monolayer materials taken from JARVIS-DFT database. We obtained very accurate ML models using gradient boosting algorithm. Then we use the trained models to discover exfoliable 2D-layered materials satisfying specific property requirements. Additionally, we integrate our formation energy ML model with a genetic algorithm for structure search to verify if the ML model reproduces the DFT convex hull. This verification establishes a more stringent evaluation metric for the ML model than what commonly used in data sciences. Our learnt model is publicly available on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml) property predictions of generalized materials.

Subfields: Materials Chemistry

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In Materials Chemistry

1. Chi Chen
2. Shyue Ping Ong
3. Yunxing Zuo
4. Weike Ye
5. Chen Zheng
6. Gerbrand Ceder

Citations in the corpus

• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (2018, methodology): “54 The MAEs of the JARVIS-ML models 53 for formation energy, band gap, bulk moduli and shear moduli are 0.12 eV atom − 1 , 0.32 eV, 10.5 GPa and 9.5 GPa, respectively, while the MAEs of AFLOW-ML models 54 for band gap, bulk moduli and shear moduli are 0.35 eV, 8.68 GPa and 10.62 GPa, respectively.”
• Recent advances and applications of deep learning methods in materials science (2021, unknown): “com/usnistgov/jarvis/ [74] Atom2vec https://github.”