Please wait a minute...
Acta Metall Sin  2024, Vol. 60 Issue (10): 1379-1387    DOI: 10.11900/0412.1961.2024.00147
Overview Current Issue | Archive | Adv Search |
Zentropy Theory: Bridging Materials Gene to Materials Properties
LIAO Mingqing1, WANG William Yi2(), WANG Yi3, SHANG Shun-Li3, LIU Zi-Kui3()
1 School of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China
2 State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, China
3 Department of Materials Science and Engineering, the Pennsylvania State University, University Park, PA, 16802, USA
Cite this article: 

LIAO Mingqing, WANG William Yi, WANG Yi, SHANG Shun-Li, LIU Zi-Kui. Zentropy Theory: Bridging Materials Gene to Materials Properties. Acta Metall Sin, 2024, 60(10): 1379-1387.

Download:  HTML  PDF(1381KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Entropy is an important concept in science and is ubiquitous from quantum to astronomy. By integrating statistical mechanics, quantum mechanics, and thermodynamics, Professor Zi-Kui Liu proposed the zentropy theory, which stacks entropy over configurations. The zentropy theory takes the configurations in Gibbs' statistical mechanics of a given ensemble as the material gene with the ground state as the basic configuration and additional configurations ergodically derived from its internal degrees of freedom. In the zentropy theory, the total entropy of a system is defined as the weighted average of the entropy of each configuration plus the statistical entropy among all configurations. In this paper, the basic equations and principles of the zentropy theory are introduced, and their typical applications, including magnetic and ferroelectric transformations, thermal expansion mechanisms, and critical phenomenon prediction are outlined. Furthermore, a perspective on the development of this theory, software ecosystems, high-throughput computing, and integration with artificial intelligence is provided in this study.

Key words:  zentropy theory      thermodynamics      materials genome engineering      thermal expansion mechanism      ferroelectric transition temperature      order-disorder transition     
Received:  08 May 2024     
ZTFLH:  O414  
Fund: Natural Science Foundation of Jiangsu Province(BK20230673);Doctor of Entrepreneurship and Innovation of Jiangsu Province(JSSCBS20221270)
Corresponding Authors:  WANG William Yi, professor, Tel: (029)88460294, E-mail: wywang@nwpu.edu.cn;
LIU Zi-Kui, professor, Tel: (814)8651934, E-mail: zxl15@psu.edu

URL: 

https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00147     OR     https://www.ams.org.cn/EN/Y2024/V60/I10/1379

Fig.1  Schematic of entropy over multiscale
Fig.2  Schematic of zentropy theory (kB—Boltzmann constant; pk and pi —probabilities of configuration k and its sub-configuration i; Sk and Ski —entropies of configuration k and its sub-configuration i)
PropertyStatistical mechanicsZentropy framework
EntropyS=-kBkpklnpkS=kpkSk-kBkpklnpk
Free energyF=kpkEk+kBTkpklnpkF=kpkFk+kBTkpklnpk
Partition functionZ=exp(-FkBT)=kexp(-EkkBT)Z=exp(-FkBT)=kexp(-FkkBT)
Probabilitypk=ZkZ=exp(-Ek-FkBT)pk=ZkZ=exp(-Fk-FkBT)
Table 1  Comparison of thermodynamic terminology between zentropy theory and classic statistical mechanics[41]
Fig.3  Volume-energy curves at 0 K of different configurations[30] (FM, AFM, and NM mean ferromagnetic, antiferromagnetic, and nonmagnetic, respectively; V and Etot mean atomic volume and total energy at 0 K, respectively)
(a) Ce (b) Fe3Pt
Fig.4  Temperature-volume phase diagram with isobaric volumes at various pressures[45] (T means temperature; the volume (V) is normalized to their respective equilibrium volume (VN) at atmospheric pressure and room temperature; CPTE and NTE mean colossal positive thermal expansion and negative thermal expansion, respectively; Exp. means experimental results)
(a) Ce (b) Fe3Pt
Fig.5  Probability of configurations as a function of temperature in PbTiO3[47] (p—probability, DW—domain wall, FEG—ground state of ferroelectric without DW, TC—critical temperature, the domain wall energies for 90DW and 180DW are 35 and 132 mJ/m2, respectively, which is taken from Ref.[58])
Fig.6  Predicted degree of disorder (fDoDS) as a function of temperature in Fe3Pt via entropy[46]E0 and Ek (V) are equilibrium energy and static total energy at 0 K, respectively. QHA means quasiharmonic approach, in which the free energy with contributions from vibrations and thermal electrons is evaluated)
1 Gan Y. Research on the innovative development of new materials science and technology in China [J]. Engineering, 2024, 32: 10
2 Xie M, Gan Y, Wang H. Research on new material power strategy by 2035 [J]. Strategic Study CAE, 2020, 22(5): 1
谢 曼, 干 勇, 王 慧. 面向2035的新材料强国战略研究 [J]. 中国工程科学, 2020, 22(5): 1
3 Liu Z K. Perspective on materials genome [J]. Chin. Sci. Bull., 2013, 58: 3618
刘梓葵. 关于材料基因组的基本观点及展望 [J]. 科学通报, 2013, 58: 3618
4 National Science and Technology Council. Materials genome initiative for global competitiveness [EB/OL]. (2011-06-24).
5 National Science and Technology Council. Materials genome initiative strategic plan [EB/OL]. (2014-12-04).
6 Su Y J, Fu H D, Bai Y, et al. Progress in materials genome engineering in China [J]. Acta. Metall. Sin., 2020, 56: 1313
doi: 10.11900/0412.1961.2020.00199
宿彦京, 付华栋, 白 洋 等. 中国材料基因工程研究进展 [J]. 金属学报, 2020, 56: 1313
doi: 10.11900/0412.1961.2020.00199
7 National Research Council. Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security [M]. Washington, D.C.: The National Academies Press, 2008: 9
8 Wang W Y, Yin J L, Chai Z X, et al. Big data-assisted digital twins for the smart design and manufacturing of advanced materials: From atoms to products [J]. J. Mater. Inf., 2022, 2: 1
9 Liu Z K, Chen L Q, Raghavan P, et al. An integrated framework for multi-scale materials simulation and design [J]. J. Comput. Aided Mater. Des., 2004, 11: 183
10 Liu X, Furrer D, Kosters J, et al. Vision 2040 : A roadmap for integrated, multiscale modeling and simulation of materials and systems [EB/OL]. (2018-03-22).
11 National Academies of Sciences, Engineering, and Medicine. NSF Efforts to Achieve the Nation's Vision for the Materials Genome Initiative: Designing Materials to Revolutionize and Engineer Our Future (DMREF) [M]. Washington, D.C.: The National Academies Press, 2023: 57
12 Xie J X. Prospects of materials genome engineering frontiers [J]. Mater. Genome Eng. Adv., 2023, 1: e17
13 Xie J X, Su Y J, Zhang D W, et al. A vision of materials genome engineering in China [J]. Engineering, 2022, 10: 10
14 Li M X, Zhao S F, Lu Z, et al. High-temperature bulk metallic glasses developed by combinatorial methods [J]. Nature, 2019, 569: 99
15 Lu Z C, Zhang Y B, Li W Y, et al. Materials genome strategy for metallic glasses [J]. J. Mater. Sci. Technol., 2023, 166: 173
doi: 10.1016/j.jmst.2023.04.074
16 Wang G J, Peng L Y, Li K Q, et al. ALKEMIE: An intelligent computational platform for accelerating materials discovery and design [J]. Comput. Mater. Sci., 2021, 186: 110064
17 Wang G J, Li K Q, Peng L Y, et al. High-throughput automatic integrated material calculations and data management intelligent platform and the application in novel alloys [J]. Acta. Metall. Sin., 2022, 58: 75
doi: 10.11900/0412.1961.2021.00041
王冠杰, 李开旗, 彭力宇 等. 高通量自动流程集成计算与数据管理智能平台及其在合金设计中的应用[J]. 金属学报, 2022, 58: 75
doi: 10.11900/0412.1961.2021.00041
18 Chong X Y, Yu W, Liang Y X, et al. Understanding oxidation resistance of Pt-based alloys through computations of Ellingham diagrams with experimental verifications [J]. J. Mater. Inf., 2023, 3: 21
19 Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science [J]. APL Mater., 2016, 4: 053208
20 Liu Y L, Niu C, Wang Z, et al. Machine learning in materials genome initiative: A review [J]. J. Mater. Sci. Technol., 2020, 57: 113
doi: 10.1016/j.jmst.2020.01.067
21 E W N. AI helps to establish a new paradigm for scientific research [J]. Bull. Chin. Acad. Sci., 2024, 39(1): 10
鄂维南. AI助力打造科学研究新范式 [J]. 中国科学院院刊, 2024, 39(1): 10
22 Li G J. AI4R: The fifth scientific research paradigm [J]. Bull. Chin. Acad. Sci., 2024, 39: 1
李国杰. 智能化科研(AI4R):第五科研范式 [J]. 中国科学院院刊, 2024, 39: 1
23 Ågren J. The materials genome and CALPHAD [J]. Chin. Sci. Bull., 2013, 58: 3633
Ågren J. 材料基因组与相图计算 [J]. 科学通报, 2013, 58: 3633
24 Wang S Q, Ye H Q. First-principles calculation of crystalline materials genome [J]. Chin. Sci. Bull., 2013, 58: 3623
王绍青, 叶恒强. 晶体材料基因组问题第一原理计算研究 [J]. 科学通报, 2013, 58: 3623
25 Liu Z K. Computational thermodynamics and its applications [J]. Acta Mater., 2020, 200: 745
26 Liu Z K. First-principles calculations and CALPHAD modeling of thermodynamics [J]. J. Phase Equilib. Diffus., 2009, 30: 517
27 Olson G B, Liu Z K. Genomic materials design: CALculation of PHAse Dynamics [J]. Calphad, 2023, 82: 102590
28 Liu Z K. Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives [J]. Calphad, 2023, 82: 102580
29 Campbell C E, Kattner U R, Liu Z K. File and data repositories for next generation CALPHAD [J]. Scr. Mater., 2014, 70: 7
30 Liu Z K, Wang Y, Shang S L. Zentropy theory for positive and negative thermal expansion [J]. J. Phase Equilib. Diffus., 2022, 43: 598
31 Liu Z K. Building materials genome from ground‐state configuration to engineering advance [J]. Mater. Genome Eng. Adv., 2023, 1: e15
32 Liu Z K, Hew N L E, Shang S L. Zentropy theory for accurate prediction of free energy, volume, and thermal expansion without fitting parameters [J]. Microstructures, 2024, 4: 2024009
33 Mooraj S, Chen W. A review on high-throughput development of high-entropy alloys by combinatorial methods [J]. J. Mater. Inf., 2023, 3: 4
34 Jiang B B, Yu Y, Cui J, et al. High-entropy-stabilized chalcogenides with high thermoelectric performance [J]. Science, 2021, 371: 830
doi: 10.1126/science.abe1292 pmid: 33602853
35 Liu Y, Lu Y H, Wang W Y, et al. Effects of solutes on thermodynamic properties of (TMZrU)C (TM = Ta, Y) medium-entropy carbides: A first-principles study [J]. J. Mater. Inf., 2023, 3: 17
36 Liao M Q, Gong H S, Qu N, et al. CALPHAD aided mechanical properties screening in full composition space of NbC-TiC-VC-ZrC ultra-high temperature ceramics [J]. Int. J. Refract. Met. Hard Mater., 2023, 113: 106191
37 Wang J, Chong X Y, Lv L, et al. High-entropy ferroelastic (10RE0.1)TaO4 ceramics with oxygen vacancies and improved thermophysical properties [J]. J. Mater. Sci. Technol., 2023, 157: 98
38 Chen L X, Chen Z W, Yao X, et al. High-entropy alloy catalysts: High-throughput and machine learning-driven design [J]. J. Mater. Inf., 2022, 2: 19
39 Ceder G. A derivation of the Ising model for the computation of phase diagrams [J]. Comput. Mater. Sci., 1993, 1: 144
40 Liu Z K. Theory of cross phenomena and their coefficients beyond Onsager theorem [J]. Mater. Res. Lett., 2022, 10: 393
41 Liu Z K. Quantitative predictive theories through integrating quantum, statistical, equilibrium, and nonequilibrium thermodynamics [J]. J. Phys.: Condens. Matter., 2024, 36: 343003
42 Liu Z K. On Gibbs Equilibrium and hillert nonequilibrium thermodynamics [DB/OL]. arXiv: 2402. 14231, 2024
43 Wang Y, Liao M Q, Bocklund B J, et al. DFTTK: Density functional theory toolKit for high-throughput lattice dynamics calculations [J]. Calphad, 2021, 75: 102355
44 Liu Z K, Li B, Lin H. Multiscale entropy and its implications to critical phenomena, emergent behaviors, and information [J]. J. Phase Equilib. Diffus., 2019, 40: 508
45 Liu Z K, Wang Y, Shang S L. Thermal expansion anomaly regulated by entropy [J]. Sci. Rep., 2014, 4: 7043
46 Shang S L, Wang Y, Liu Z K. Quantifying the degree of disorder and associated phenomena in materials through zentropy: Illustrated with Invar Fe3Pt [J]. Scr. Mater., 2023, 225: 115164
47 Liu Z K, Shang S L, Du J L, et al. Parameter-free prediction of phase transition in PbTiO3 through combination of quantum mechanics and statistical mechanics [J]. Scr. Mater., 2023, 232: 115480
48 Liang E J, Sun Q, Yuan H L, et al. Negative thermal expansion: Mechanisms and materials [J]. Front. Phys., 2021, 16: 53302
49 Zhou C, Tang Z Y, Kong X Q, et al. High-performance zero thermal expansion in Al metal matrix composites [J]. Acta Mater., 2024, 275: 120076
50 Liao M Q, Wang Y, Wang F J, et al. Unexpected low thermal expansion coefficients of pentadiamond [J]. Phys. Chem. Chem. Phys., 2022, 24: 23561
51 Wang Y, Hector Jr L G, Zhang H, et al. A thermodynamic framework for a system with itinerant-electron magnetism [J]. J. Phys.: Condens. Matter., 2009, 21: 326003
52 Wang Y, Shang S L, Zhang H, et al. Thermodynamic fluctuations in magnetic states: Fe3Pt as a prototype [J]. Philos. Mag. Lett., 2010, 90: 851
53 Shang S L, Wang Y, Liu Z K. Thermodynamic fluctuations between magnetic states from first-principles phonon calculations: The case of bcc Fe [J]. Phys. Rev., 2010, 82B: 014425
54 Shang S L, Saal J E, Mei Z G, et al. Magnetic thermodynamics of fcc Ni from first-principles partition function approach [J]. J. Appl. Phys., 2010, 108: 123514
55 Liu Z K, Wang Y, Shang S L. Origin of negative thermal expansion phenomenon in solids [J]. Scr. Mater., 2011, 65: 664
56 Wang Y, Hector Jr L G, Zhang H, et al. Thermodynamics of the Ce γ-α transition: Density-functional study [J]. Phys. Rev, 2008, 78B: 104113
57 Li Y L, Hu S Y, Liu Z K, et al. Effect of substrate constraint on the stability and evolution of ferroelectric domain structures in thin films [J]. Acta Mater., 2002, 50: 395
58 Meyer B, Vanderbilt D. Ab initio study of ferroelectric domain walls in PbTiO3 [J]. Phys. Rev., 2002, 65B: 104111
59 Fang H Z, Wang Y, Shang S L, et al. Nature of ferroelectric-paraelectric phase transition and origin of negative thermal expansion in PbTiO3 [J]. Phys. Rev., 2015, 91B: 024104
60 Du J L, Malyi O I, Shang S L, et al. Density functional thermodynamic description of spin, phonon and displacement degrees of freedom in antiferromagnetic-to-paramagnetic phase transition in YNiO3 [J]. Mater. Today Phys., 2022, 27: 100805
61 Du J L, Zhang Z L, Shang S L, et al. Underpinnings behind the magnetic order-to-disorder transition and property anomaly of disproportionated insulating samarium nickelate [J]. Acta Mater., 2024, 268: 119783
62 Liu Z K, Shang S L. Revealing symmetry-broken superconducting configurations by density functional theory [DB/OL]. arXiv: 2404. 00719, 2024
63 Hong Q J, Liu Z K. A generalized approach for rapid entropy calculation of liquids and solids [DB/OL]. arXiv: 2403. 19872, 2024
64 Bocklund B, Otis R, Egorov A, et al. ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: Application to Cu-Mg [J]. MRS Commun., 2019, 9: 618
doi: 10.1557/mrc.2019.59
65 Otis R, Liu Z K. pycalphad: CALPHAD-based computational thermodynamics in python [J]. J. Open Res. Softw., 2017, 5: 1
66 Paulson N H, Bocklund B J, Otis R A, et al. Quantified uncertainty in thermodynamic modeling for materials design [J]. Acta Mater., 2019, 174: 9
doi: 10.1016/j.actamat.2019.05.017
67 Peng J, Lee S, Williams A, et al. Advanced data science toolkit for non-data scientists—A user guide [J]. Calphad, 2020, 68: 101733
68 Krajewski A M, Siegel J W, Xu J, et al. Extensible structure-informed prediction of formation energy with improved accuracy and usability employing neural networks [J]. Comput. Mater. Sci., 2022, 208: 111254
69 Liu Z K. Ocean of data: Integrating first-principles calculations and CALPHAD modeling with machine learning [J]. J. Phase Equilib. Diffus., 2018, 39: 635
70 Liu Z K. View and comments on the data ecosystem: “Ocean of data” [J]. Engineering, 2020, 6: 604
[1] WANG Guanjie, LIU Shengxian, ZHOU Jian, SUN Zhimei. Explainable Machine Learning in the Research of Materials Science[J]. 金属学报, 2024, 60(10): 1345-1361.
[2] ZHANG Yuexin, WANG Jujin, YANG Wen, ZHANG Lifeng. Effect of Cooling Rate on the Evolution of Nonmetallic Inclusions in a Pipeline Steel[J]. 金属学报, 2023, 59(12): 1603-1612.
[3] GAO Jianbao, LI Zhicheng, LIU Jia, ZHANG Jinliang, SONG Bo, ZHANG Lijun. Current Situation and Prospect of Computationally Assisted Design in High-Performance Additive Manufactured Aluminum Alloys: A Review[J]. 金属学报, 2023, 59(1): 87-105.
[4] WANG Guanjie, LI Kaiqi, PENG Liyu, ZHANG Yiming, ZHOU Jian, SUN Zhimei. High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys[J]. 金属学报, 2022, 58(1): 75-88.
[5] LIU Feng, WANG Tianle. Precipitation Modeling via the Synergy of Thermodynamics and Kinetics[J]. 金属学报, 2021, 57(1): 55-70.
[6] SU Yanjing, FU Huadong, BAI Yang, JIANG Xue, XIE Jianxin. Progress in Materials Genome Engineering in China[J]. 金属学报, 2020, 56(10): 1313-1323.
[7] WANG Zumin,ZHANG An,CHEN Yuanyuan,HUANG Yuan,WANG Jiangyong. Research Progress on Fundamentals and Applications of Metal-Induced Crystallization[J]. 金属学报, 2020, 56(1): 66-82.
[8] Chengming ZHENG, Qingchao TIAN. Effect of Alloy Elements on Oxidation Behavior of Piercing Plug Steel[J]. 金属学报, 2019, 55(4): 427-435.
[9] Liheng LIU,Chunshan CHE,Gang KONG,Jintang LU,Shuanghong ZHANG. DESTABILIZATION MECHANISM OF Fe-Al INHIBITION LAYER IN Zn-0.2%Al HOT-DIP GALVANIZING COATING AND RELATED THERMODYNAMIC EVALUATION[J]. 金属学报, 2016, 52(5): 614-624.
[10] Feng LIU, Kang WANG. DISCUSSIONS ON THE CORRELATION BETWEEN THERMODYNAMICS AND KINETICS DURING THE PHASE TRANSFORMATIONS IN THE TMCP OF LOW-ALLOY STEELS[J]. 金属学报, 2016, 52(10): 1326-1332.
[11] XIE Jun, YU Jinjiang, SUN Xiaofeng, JIN Tao, SUN Yuan. CARBIDE EVOLUTION BEHAVIOR OF K416B AS-CAST Ni-BASED SUPERALLOY WITH HIGH W CONTENT DURING HIGH TEMPERATURE CREEP[J]. 金属学报, 2015, 51(4): 458-464.
[12] WU Changjun, ZHU Chenlu, SU Xuping, LIU Ya, PENG Haoping, WANG Jianhua. THERMODYNAMICAL AND KINETIC INVESTIGA-TION OF FORMATION OF PERIODIC LAYERED STRUCTURE IN TiCu/Zn INTERFACE REACTION[J]. 金属学报, 2014, 50(8): 930-936.
[13] MA Ping, WU Erdong, LI Wuhui, SUN Kai, CHEN Dongfeng. MICROSTRUCTURES AND HYDROGEN STORAGE PROPERTIES OF Ti0.7Zr0.3(Cr1-xVx)2 ALLOYS[J]. 金属学报, 2014, 50(4): 454-462.
[14] WANG Bin, LIU Zhenyu, ZHOU Xiaoguang, WANG Guodong. CALCULATION OF TRANSFORMATION DRIVING FORCE FOR THE PRECIPITATION OF NANO-SCALED CEMENTITES IN THE HYPOEUTECTOID STEELS THROUGH ULTRA FAST COOLING[J]. 金属学报, 2013, 49(1): 26-34.
[15] LI Wuhui TIAN Baohong MA Ping WU Erdong. HYDROGEN STORAGE PROPERTIES OF ScMn2 ALLOY[J]. 金属学报, 2012, 48(7): 822-829.
No Suggested Reading articles found!