Machine learning in materials design: Algorithm and application

doi:10.1088/1674-1056/abc0e3

中国物理B ›› 2020, Vol. 29 ›› Issue (11): 116103-.doi: 10.1088/1674-1056/abc0e3

所属专题： SPECIAL TOPIC — Machine learning in condensed matter physics

Zhilong Song(宋志龙)¹, Xiwen Chen(陈曦雯)¹, Fanbin Meng(孟繁斌)¹, Guanjian Cheng(程观剑)¹, Chen Wang(王陈)¹, Zhongti Sun(孙中体)¹^,†(), Wan-Jian Yin(尹万健)¹^,^‡()

收稿日期:2020-07-06 修回日期:2020-08-24 接受日期:2020-10-14 出版日期:2020-11-05 发布日期:2020-11-03

Machine learning in materials design: Algorithm and application

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体)^†, and Wan-Jian Yin(尹万健)^‡

College of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow University, Suzhou 215006, China

Received:2020-07-06 Revised:2020-08-24 Accepted:2020-10-14 Online:2020-11-05 Published:2020-11-03
Contact: ^†Corresponding author. E-mail: ztsun@suda.edu.cn ^‡Corresponding author. E-mail: wjyin@suda.edu.cn
Supported by:
Project support by the National Natural Science Foundation of China (Grant Nos. 11674237 and 51602211), the National Key Research and Development Program of China (Grant No. 2016YFB0700700), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China, and China Post-doctoral Foundation (Grant No. 7131705619).

摘要/Abstract

Abstract:

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

Key words: machine learning, materials design, structure-property relationship, active learning

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体), Wan-Jian Yin(尹万健). [J]. 中国物理B, 2020, 29(11): 116103-.

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体), and Wan-Jian Yin(尹万健). Machine learning in materials design: Algorithm and application[J]. Chin. Phys. B, 2020, 29(11): 116103-.

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Name	Function	URL
Pymatgen	Robust, open-source python library for materials analysis	https://pymatgen.org
AFLOW π	A minimalist framework for high-throughput first principles calculations	http://aflowlib.org/src/aflowpi
FireWorks	An open-source code for defining, managing, and executing calculation workflows	https://materialsproject.github.io/fireworks
AiiDA	A workflow to automate complex numerical procedures of calculation	http://www.aiida.net
Pymatflow	A workflow simplifier for research on materials science by means of ab initio simulation	http://pymatflow.readthedocs.org
ASE	Setting up, steering, and analyzing atomistic simulations	https://wiki.fysik.dtu.dk/ase
Atomate	Built on top of state-of-the-art open-source libraries: pymatgen, custodian, and FireWorks	https://atomate.org
Custodian	A simple, robust, and flexible just-in-time (JIT) job management framework	https://pypi.org/project/custodian
MPInterfaces	A python tool that enables high throughput analysis of interfaces using VASP, VASPsol, and MP tools	http://henniggroup.github.io/MPInterfaces
Imeall	A database framework for the calculation of the atomistic properties of grain boundaries	https://github.com/Montmorency/imeall
Pylada	A modular python framework to control physics simulations	http://pylada.github.io/pylada
Pyiron	An integrated development environment (IDE) for computational materials science	https://pyiron.github.io

Name	Data type	URL	Free
Materials Project	Multiple	https://materialsproject.org	√
ICSD	Inorganic & Experimental	https://icsd.fiz-karlsruhe.de	×
AFLOWLIB	Inorganic & Computational	http://aflowlib.org	√
COD	Multiple & Experimental	http://crystallography.net	√
QM9	Organic molecules	http://quantum-machine.org/datasets/	√
OMDB-GAP1	Organic crystals	https://omdb.mathub.io/dataset	√
OQMD	Multiple & Computational	http://oqmd.org	√
NOMAD	Multiple	https://nomad-repository.eu	√
Materials Cloud	Multiple (3D, 2D)	https://www.materialscloud.org	√
NREL Materials	Computational & Renewable	https://materials.nrel.gov	√
Clean Energy Project	Solar cell	https://cepdb.molecularspace.org	×
TEDesignLab	Thermoelectric	http://www.tedesignlab.org	√
HTEM	Inorganic	https://htem.nrel.gov/	√
Supercon	Superconducting	https://supercon.nims.go.jp	√
MaterialsWeb	2D	https://www.materialsweb.org	√
C2DB	2D	https://cmr.fysik.dtu.dk/c2db/c2db.html	√
CSD	Multiple	https://www.ccdc.cam.ac.uk	×
CMR	Multiple (3D, 2D)	https://cmr.fysik.dtu.dk	√
Citrination	Multiple	https://citrination.com	√
JARVIS-DFT	2D	https://www.ctcms.nist.gov/∼knc6/JVASP.html	√
MatNavi	Multiple	https://mits.nims.go.jp	√
MatWeb	Engineering	http://matweb.com	√
GDB	Small organic molecules	http://gdb.unibe.ch	√
ZINC	Compounds	https://zinc15.docking.org	√
ChEMBL	Bioactive molecules	https://www.ebi.ac.uk/chembl	√
ChemSpider	Multiple	http://www.chemspider.com	√
Materials Commons	Computational	https://materialscommons.org	√
AiiDA	Alloy Phase Diagram	http://www.aiida.net	√
ASM	Inorganic	https://www.asminternational.org	×
LPF	Multiple	https://paulingfile.com	×
PCD	Multiple	http://www.crystalimpact.com/pcd	×
Nano-HUB	Nanomaterials	http://nanohub.org	√
EELS Data Base	Spectra	https://eelsdb.eu	√
XAFS database	Spectra	https://www.cat.hokudai.ac.jp/catdb/	√

Name	Description	URL
QML	A python toolkit for representation learning of properties of molecules and solids	https://www.qmlcode.org
AMP	A modular approach to machine learning in atomistic simulations	https://amp.readthedocs.io
Magpie	Materials-agnostic platform for informatics and exploration	https://github.com/amarkrishna/demo1
RDkit	A collection of cheminformatics and machine-learning software written in C++ and Python	https://github.com/rdkit/rdkit
ChemML	A ML program suite for the analysis, mining, and modeling of chemical and materials data	https://pypi.org/project/chemml
DScribe	Library of descriptors for machine learning in materials science	https://singroup.github.io/dscribe
Matminer	A Python library for data mining the properties of materials	https://hackingmaterials.lbl.gov/matminer
SchNet	A deep learning architecture for quantum chemistry	https://github.com/atomistic-machine-learning/SchNet
DeepChem	Deep-learning models for drug discovery and quantum chemistry	http://github.com/deepchem/deepchem
MEGNet	An implementation of DeepMind’s graph networks for universal machine learning in materials science	https://github.com/materialsvirtuallab/megnet
CGCNN	Implement crystal graph convolutional neural networks to arbitrary crystal structures	https://github.com/txie-93/cgcnn

	Conventional	Deep learning	Tree-based	Descriptor	Active	Unsupervised
Thermodynamic stability	[86,188–193]	[26]	[188–[190,194–196]	[195]		[86]
Band gap	[31,193,197–204]	[20,203,205]	[31,196,201,204]		[33]	[206]
Superconductivity	[207–210]	[211,212]	[19,213,214]	[215]		[207,210,213]
Thermal conductivity	[79,216–221]	[79,218,222,223]	[79,224–227]		[216,222,228–230]	[216]
Curie temperature	[6,231–236]		[235–237]		[6]	[233,236]
Bulk and shear moduli	[238–242]	[97,102,243]	[25,240,244–246]	[246]
Debye temperature and heat capacity	[239,242,247,248]		[25,248]
Density of states	[87,249,250]	[251]				[249,252]
Dielectric breakdown strength	[23,253–255]		[255]	[253]
grain boundary structure and properties	[256–259]	[260]	[257,258]		[261,262]	[263]
Lattice parameter	[264–266]		[266]
Lithium ion batteries SOC and conduction	[22,267–274]	[275–277]	[22,273,278]
melting temperature	[221,279–282]	[279]				[279]

Machine learning in materials design: Algorithm and application

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