[1] |
MITTASCH A, FRANKENBURG W. Early studies of multicomponent catalysts [J]. Advances in Catalysis, 1950, 2(6): 81-104.
|
[2] |
SINFELT J H. Catalysis by alloys and bimetallic clusters [J]. Accounts of Chemical Research, 1977, 10(1): 15-20.
|
[3] |
SINFELT J H. Structure of metal catalysts [J]. Reviews of Modern Physics, 1979, 51(3): 569.
|
[4] |
XU F Y, DENG S B, XU J, et al. Highly active and stable Ni-Fe bimetal prepared by ball milling for catalytic hydrodechlorination of 4-chlorophenol[J]. Environmental Science & Technology, 2012, 46(8): 4576-4582.
|
[5] |
HUANG X, LI Y, et al. Synthesis of PtPd bimetal nanocrystals with controllable shape, composition, and their tunable catalytic properties[J]. Nano Letters, 2012, 12(8): 4265-4270.
|
[6] |
PARAMSOTHY M, GUPTA M, SRIKANTH N. Processing, microstructure, and properties of a Mg/Al bimetal macrocomposite[J]. Journal of Composite Materials, 2008, 42(24): 2567-2584.
|
[7] |
GAO W L, JIN R C, CHEN J X, et al. Titania-supported bimetallic catalysts for photocatalytic reduction of nitrate[J]. Catalysis Today, 2004, 90(3/4): 331-336.
|
[8] |
FERNNDEZ J L, WALSH D A, BARD A J. Thermodynamic guidelines for the design of bimetallic catalysts for oxygen electroreduction and rapid screening by scanning electrochemical microscopy. M-Co (M: Pd, Ag, Au)[J]. Journal of the American Chemical Society, 2005, 127(1): 357-365.
|
[9] |
STAMENKOVIC V R, FOWLER B, MUN B S, et al. Improved oxygen reduction activity on Pt3Ni(111) via increased surface site availability[J]. Science, 2007, 315(5811): 493-497.
|
[10] |
CRISAFULLI C, SCIRE S, MAGGIORE R, et al. CO2 reforming of methane over Ni-Ru and Ni-Pd bimetallic catalysts[J]. Catalysis Letters, 1999, 59(1): 21-26.
|
[11] |
DE S, ZHANG J, LUQUE R, et al. Ni-based bimetallic heterogeneous catalysts for energy and environmental applications[J]. Energy & Environmental Science, 2016, 9(11): 3314-3347.
|
[12] |
LEE J H, KATTEL S, JIANG Z, et al. Tuning the activity and selectivity of electroreduction of CO2 to synthesis gas using bimetallic catalysts[J]. Nature Communications, 2019, 10(1): 1-8.
|
[13] |
SCHWAB G. Some new aspects of the strength of alloys[J]. Transactions of the Faraday Society, 1949, 45:385-396.
|
[14] |
WANG C P, LIU X J, OHNUMA I, et al. Formation of immiscible alloy powders with egg-type microstructure[J]. Science, 2002, 297(5583): 990-993.
|
[15] |
COLINET C. High temperature calorimetry: Recent developments[J]. Journal of Alloys and Compounds, 1995,220(1/2): 76-87.
|
[16] |
TOPOR L, KLEPPA O J. Standard molar enthalpy of formation of LaB6 by high-temperature calorimetry[J]. The Journal of Chemical Thermodynamics, 1984, 16(10): 993-1002.
|
[17] |
KIM G, MESCHEL S V, NASH P, et al. Experimental formation enthalpies for intermetallic phases and other inorganic compounds[J]. Scientific Data, 2017, 4(1): 170162.
|
[18] |
MIEDEMA A R, CHTEL P F D, BOER F. Cohesion in alloys — fundamentals of a semi-empirical model[J]. Physica B+C, 1980, 100(1): 1-28.
|
[19] |
MIEDEMA A R, BOER F R, CHATEL P F. Empirical description of the role of electronegativity in alloy formation[J]. Journal of Physics F: Metal Physics, 1973, 3(8): 1558.
|
[20] |
ZHANG R F, RAJAN K. Statistically based assessment of formation enthalpy for intermetallic compounds[J]. Chemical Physics Letters, 2014, 612: 177-181.
|
[21] |
ZHANG R F, ZHANG S H, HE Z J, et al. Miedema calculator: A thermodynamic platform for predicting formation enthalpies of alloys within framework of Miedema’s Theory [J]. Computer Physics Communications,2016, 209: 58-69.
|
[22] |
PALINA N, SAKATA O, KUMARA L, et al. Electronic structure evolution with composition alteration of RhxCuy alloy nanoparticles[J]. Scientific Reports, 2017, 7(1): 1-9.
|
[23] |
FOILES S M, BASKES M I, DAW M S. Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys[J]. Physical Review B, 1986, 33(12): 7983.
|
[24] |
JOHNSON R A. Alloy models with the embedded-atom method[J]. Physical Review B, 1989, 39(17): 12554.
|
[25] |
LEI Y W, SUN X R, ZHOU R L, et al. Embedded atom method potentials for Ce-Ni binary alloy[J]. Computational Materials Science, 2018, 150: 1-8.
|
[26] |
WILLIAMS A R, GELATT C D, MORUZZI V L. Microscopic basis of miedema’s empirical theory of transition-metal compound formation[J]. Physical Review Letters, 1980, 44(11): 394-395.
|
[27] |
ZHANG Y, KRESSE G, WOLVERTON C. Nonlocal first-principles calculations in Cu-Au and other intermetallic alloys[J]. Physical Review Letters, 2014, 112(7): 75502.
|
[28] |
FARSI L, DESKINS N A. First principles analysis of surface dependent segregation in bimetallic alloys[J]. Physical Chemistry Chemical Physics, 2019, 21(42): 23626-23637.
|
[29] |
REITH D, PODLOUCKY R. First-principles model study of the phase stabilities of dilute Fe-Cu alloys: Role of vibrational free energy[J]. Physical Review B, 2009, 80(5): 54108.
|
[30] |
OU L. The origin of enhanced electrocatalytic activity of Pt-M (M= Fe, Co, Ni, Cu, and W) alloys in PEM fuel cell cathodes: A DFT computational study[J]. Computational and Theoretical Chemistry, 2014, 1048: 69-76.
|
[31] |
GUBAEV K, PODRYABINKIN E V, HART G L, et al. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials[J]. Computational Materials Science, 2019, 156: 148-156.
|
[32] |
DOAK J W, HAO S, KIRKLIN S, et al. Computational prediction of nanostructured alloys with enhanced thermoelectric properties[J]. Physical Review Materials, 2019, 3(10): 105404.
|
[33] |
JAIN A, ONG S P, HAUTIER G, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation[J]. APL Materials, 2013, 1(1): 11002.
|
[34] |
SAAL J E, KIRKLIN S, AYKOL M, et al. Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD)[J]. JOM, 2013, 65(11): 1501-1509.
|
[35] |
WU X D, ZHU X Q, WU G Q, et al. Data mining with big data[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 26(1): 97-107.
|
[36] |
ROSCHER R, BOHN B, DUARTE M F, et al. Explainable machine learning for scientific insights and discoveries[J]. IEEE Access, 2020, 8: 42200- 42216.
|
[37] |
LI Z, WANG S W, CHIN W S, et al. High-throughput screening of bimetallic catalysts enabled by machine learning[J]. Journal of Materials Chemistry A, 2017, 5(46): 24131-24138.
|
[38] |
GHIRINGHELLI L M, VYBIRAL J, LEVCHENKO S V, et al. Big data of materials science: Critical role of the descriptor[J]. Physical Review Letters, 2015, 114(10): 105503.
|
[39] |
RUBAN A, HAMMER B, STOLTZE P, et al. Surface electronic structure and reactivity of transition and noble metals[J]. Journal of Molecular Catalysis A: Chemical, 1997, 115(3): 421-429.
|
[40] |
ZHAO Z J, LIU S H, ZHA S J, et al. Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors[J]. Nature Reviews Materials, 2019, 4: 792-804.
|
[41] |
CHOI K, JANG Y, CHUNG D Y, et al. A simple synthesis of urchin-like Pt-Ni bimetallic nanostructures as enhanced electrocatalysts for the oxygen reduction reaction[J]. Chemical Communications, 2016, 52(3): 597-600.
|
[42] |
LI H, WEN P, LI Q, et al. Earth-abundant iron diboride (FeB2) nanoparticles as highly active bifunctional electrocatalysts for overall water splitting[J]. Advanced Energy Materials, 2017, 7(17): 1700513.
|
[43] |
YUAN W Y, ZHAO X S, HAO W J, et al. Performance of surface-oxidized Ni3B, Ni2B, and NiB2 electrocatalysts for overall water splitting[J]. ChemElectroChem, 2019, 6(3): 764-770.
|
[44] |
ZALUSKA A, ZALUSKI L, STRM-OLSEN J O. Synergy of hydrogen sorption in ball-milled hydrides of Mg and Mg2Ni[J]. Journal of Alloys and Compounds, 1999, 289(1/2): 197-206.
|
[45] |
ZHANG L T, CAI Z L, YAO Z D, et al. A striking catalytic effect of facile synthesized ZrMn2 nanoparticles on the de/rehydrogenation properties of MgH2[J]. Journal of Materials Chemistry A, 2019, 7(10): 5626-5634.
|
[46] |
OUYANG R H, CURTAROLO S, AHMETCIK E, et al. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates[J]. Physical Review Materials, 2018, 2: 083802.
|
[47] |
OUYANG R H, AHMETCIK E, CARBOGNO C, et al. Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO[J]. Journal of Physics: Materials, 2019, 2(2): 24002.
|
[48] |
BARTEL C J, MILLICAN S L, DEML A M, et al. Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry[J]. Nature Communications, 2018, 9: 4168.
|
[49] |
BARTEL C J, SUTTON C, GOLDSMITH B R, et al. New tolerance factor to predict the stability of perovskite oxides and halides[J]. Science Advances, 2019, 5(2): evva0693.
|
[50] |
ANDERSEN M, LEVCHENKO S V, SCHEFFLER M, et al. Beyond scaling relations for the description of catalytic materials[J]. ACS Catalysis, 2019, 9(4): 2752-2759.
|
[51] |
OUYANG R H. Exploiting ionic radii for rational design of halide perovskites[J]. Chemistry of Materials,2020, 32(1): 595-604.
|
[52] |
HAYNES W M. CRC Handbook of Chemistry and Physics[M]. Boca Raton, FL: CRC Press, 2014.
|
[53] |
MEDVEDEV V A, COX J D, WAGMAN D D. CODATA Key Values for Thermodynamics[M]. New York: Hemisphere Publishing Corporation, 1989.
|
[54] |
BLUM A L, LANGLEY P. Selection of relevant features and examples in machine learning[J]. Artificial Intelligence, 1997, 97(1/2): 245-271.
|
[55] |
BREIMAN L, SPECTOR P. Submodel selection and evaluation in regression. The X-random case[J]. International Statistical Review, 1992, 60(3): 291-319.
|
[56] |
RAO R B, FUNG G, ROSALES R. On the dangers of cross-validation. An experimental evaluation[C]// Proceedings of the 2008 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2008:588.
|
[57] |
GHIRINGHELLI L M, VYBIRAL J, AHMETCIK E, et al. Learning physical descriptors for materials science by compressed sensing[J]. New Journal of Physics, 2017, 19(2): 23017.
|
[58] |
MA X F, LI Z, ACHENIE L E K, et al. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening[J]. The Journal of Physical Chemistry Letters, 2015, 6(18): 3528-3533.
|
[59] |
吴春峰, 李慧改, 郑少波, 等. 二元合金热力学模型—Miedema模型[J].上海金属, 2011, 33(4): 1-5.
|
[60] |
WATSON R E, BENNETT L H. Optimized predictions for heats of formation of transition-metal alloys II [J]. Calphad, 1984, 8(4): 307-321.)
|
[1] |
MITTASCH A, FRANKENBURG W. Early studies of multicomponent catalysts [J]. Advances in Catalysis, 1950, 2(6): 81-104.
|
[2] |
SINFELT J H. Catalysis by alloys and bimetallic clusters [J]. Accounts of Chemical Research, 1977, 10(1): 15-20.
|
[3] |
SINFELT J H. Structure of metal catalysts [J]. Reviews of Modern Physics, 1979, 51(3): 569.
|
[4] |
XU F Y, DENG S B, XU J, et al. Highly active and stable Ni-Fe bimetal prepared by ball milling for catalytic hydrodechlorination of 4-chlorophenol[J]. Environmental Science & Technology, 2012, 46(8): 4576-4582.
|
[5] |
HUANG X, LI Y, et al. Synthesis of PtPd bimetal nanocrystals with controllable shape, composition, and their tunable catalytic properties[J]. Nano Letters, 2012, 12(8): 4265-4270.
|
[6] |
PARAMSOTHY M, GUPTA M, SRIKANTH N. Processing, microstructure, and properties of a Mg/Al bimetal macrocomposite[J]. Journal of Composite Materials, 2008, 42(24): 2567-2584.
|
[7] |
GAO W L, JIN R C, CHEN J X, et al. Titania-supported bimetallic catalysts for photocatalytic reduction of nitrate[J]. Catalysis Today, 2004, 90(3/4): 331-336.
|
[8] |
FERNNDEZ J L, WALSH D A, BARD A J. Thermodynamic guidelines for the design of bimetallic catalysts for oxygen electroreduction and rapid screening by scanning electrochemical microscopy. M-Co (M: Pd, Ag, Au)[J]. Journal of the American Chemical Society, 2005, 127(1): 357-365.
|
[9] |
STAMENKOVIC V R, FOWLER B, MUN B S, et al. Improved oxygen reduction activity on Pt3Ni(111) via increased surface site availability[J]. Science, 2007, 315(5811): 493-497.
|
[10] |
CRISAFULLI C, SCIRE S, MAGGIORE R, et al. CO2 reforming of methane over Ni-Ru and Ni-Pd bimetallic catalysts[J]. Catalysis Letters, 1999, 59(1): 21-26.
|
[11] |
DE S, ZHANG J, LUQUE R, et al. Ni-based bimetallic heterogeneous catalysts for energy and environmental applications[J]. Energy & Environmental Science, 2016, 9(11): 3314-3347.
|
[12] |
LEE J H, KATTEL S, JIANG Z, et al. Tuning the activity and selectivity of electroreduction of CO2 to synthesis gas using bimetallic catalysts[J]. Nature Communications, 2019, 10(1): 1-8.
|
[13] |
SCHWAB G. Some new aspects of the strength of alloys[J]. Transactions of the Faraday Society, 1949, 45:385-396.
|
[14] |
WANG C P, LIU X J, OHNUMA I, et al. Formation of immiscible alloy powders with egg-type microstructure[J]. Science, 2002, 297(5583): 990-993.
|
[15] |
COLINET C. High temperature calorimetry: Recent developments[J]. Journal of Alloys and Compounds, 1995,220(1/2): 76-87.
|
[16] |
TOPOR L, KLEPPA O J. Standard molar enthalpy of formation of LaB6 by high-temperature calorimetry[J]. The Journal of Chemical Thermodynamics, 1984, 16(10): 993-1002.
|
[17] |
KIM G, MESCHEL S V, NASH P, et al. Experimental formation enthalpies for intermetallic phases and other inorganic compounds[J]. Scientific Data, 2017, 4(1): 170162.
|
[18] |
MIEDEMA A R, CHTEL P F D, BOER F. Cohesion in alloys — fundamentals of a semi-empirical model[J]. Physica B+C, 1980, 100(1): 1-28.
|
[19] |
MIEDEMA A R, BOER F R, CHATEL P F. Empirical description of the role of electronegativity in alloy formation[J]. Journal of Physics F: Metal Physics, 1973, 3(8): 1558.
|
[20] |
ZHANG R F, RAJAN K. Statistically based assessment of formation enthalpy for intermetallic compounds[J]. Chemical Physics Letters, 2014, 612: 177-181.
|
[21] |
ZHANG R F, ZHANG S H, HE Z J, et al. Miedema calculator: A thermodynamic platform for predicting formation enthalpies of alloys within framework of Miedema’s Theory [J]. Computer Physics Communications,2016, 209: 58-69.
|
[22] |
PALINA N, SAKATA O, KUMARA L, et al. Electronic structure evolution with composition alteration of RhxCuy alloy nanoparticles[J]. Scientific Reports, 2017, 7(1): 1-9.
|
[23] |
FOILES S M, BASKES M I, DAW M S. Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys[J]. Physical Review B, 1986, 33(12): 7983.
|
[24] |
JOHNSON R A. Alloy models with the embedded-atom method[J]. Physical Review B, 1989, 39(17): 12554.
|
[25] |
LEI Y W, SUN X R, ZHOU R L, et al. Embedded atom method potentials for Ce-Ni binary alloy[J]. Computational Materials Science, 2018, 150: 1-8.
|
[26] |
WILLIAMS A R, GELATT C D, MORUZZI V L. Microscopic basis of miedema’s empirical theory of transition-metal compound formation[J]. Physical Review Letters, 1980, 44(11): 394-395.
|
[27] |
ZHANG Y, KRESSE G, WOLVERTON C. Nonlocal first-principles calculations in Cu-Au and other intermetallic alloys[J]. Physical Review Letters, 2014, 112(7): 75502.
|
[28] |
FARSI L, DESKINS N A. First principles analysis of surface dependent segregation in bimetallic alloys[J]. Physical Chemistry Chemical Physics, 2019, 21(42): 23626-23637.
|
[29] |
REITH D, PODLOUCKY R. First-principles model study of the phase stabilities of dilute Fe-Cu alloys: Role of vibrational free energy[J]. Physical Review B, 2009, 80(5): 54108.
|
[30] |
OU L. The origin of enhanced electrocatalytic activity of Pt-M (M= Fe, Co, Ni, Cu, and W) alloys in PEM fuel cell cathodes: A DFT computational study[J]. Computational and Theoretical Chemistry, 2014, 1048: 69-76.
|
[31] |
GUBAEV K, PODRYABINKIN E V, HART G L, et al. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials[J]. Computational Materials Science, 2019, 156: 148-156.
|
[32] |
DOAK J W, HAO S, KIRKLIN S, et al. Computational prediction of nanostructured alloys with enhanced thermoelectric properties[J]. Physical Review Materials, 2019, 3(10): 105404.
|
[33] |
JAIN A, ONG S P, HAUTIER G, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation[J]. APL Materials, 2013, 1(1): 11002.
|
[34] |
SAAL J E, KIRKLIN S, AYKOL M, et al. Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD)[J]. JOM, 2013, 65(11): 1501-1509.
|
[35] |
WU X D, ZHU X Q, WU G Q, et al. Data mining with big data[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 26(1): 97-107.
|
[36] |
ROSCHER R, BOHN B, DUARTE M F, et al. Explainable machine learning for scientific insights and discoveries[J]. IEEE Access, 2020, 8: 42200- 42216.
|
[37] |
LI Z, WANG S W, CHIN W S, et al. High-throughput screening of bimetallic catalysts enabled by machine learning[J]. Journal of Materials Chemistry A, 2017, 5(46): 24131-24138.
|
[38] |
GHIRINGHELLI L M, VYBIRAL J, LEVCHENKO S V, et al. Big data of materials science: Critical role of the descriptor[J]. Physical Review Letters, 2015, 114(10): 105503.
|
[39] |
RUBAN A, HAMMER B, STOLTZE P, et al. Surface electronic structure and reactivity of transition and noble metals[J]. Journal of Molecular Catalysis A: Chemical, 1997, 115(3): 421-429.
|
[40] |
ZHAO Z J, LIU S H, ZHA S J, et al. Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors[J]. Nature Reviews Materials, 2019, 4: 792-804.
|
[41] |
CHOI K, JANG Y, CHUNG D Y, et al. A simple synthesis of urchin-like Pt-Ni bimetallic nanostructures as enhanced electrocatalysts for the oxygen reduction reaction[J]. Chemical Communications, 2016, 52(3): 597-600.
|
[42] |
LI H, WEN P, LI Q, et al. Earth-abundant iron diboride (FeB2) nanoparticles as highly active bifunctional electrocatalysts for overall water splitting[J]. Advanced Energy Materials, 2017, 7(17): 1700513.
|
[43] |
YUAN W Y, ZHAO X S, HAO W J, et al. Performance of surface-oxidized Ni3B, Ni2B, and NiB2 electrocatalysts for overall water splitting[J]. ChemElectroChem, 2019, 6(3): 764-770.
|
[44] |
ZALUSKA A, ZALUSKI L, STRM-OLSEN J O. Synergy of hydrogen sorption in ball-milled hydrides of Mg and Mg2Ni[J]. Journal of Alloys and Compounds, 1999, 289(1/2): 197-206.
|
[45] |
ZHANG L T, CAI Z L, YAO Z D, et al. A striking catalytic effect of facile synthesized ZrMn2 nanoparticles on the de/rehydrogenation properties of MgH2[J]. Journal of Materials Chemistry A, 2019, 7(10): 5626-5634.
|
[46] |
OUYANG R H, CURTAROLO S, AHMETCIK E, et al. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates[J]. Physical Review Materials, 2018, 2: 083802.
|
[47] |
OUYANG R H, AHMETCIK E, CARBOGNO C, et al. Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO[J]. Journal of Physics: Materials, 2019, 2(2): 24002.
|
[48] |
BARTEL C J, MILLICAN S L, DEML A M, et al. Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry[J]. Nature Communications, 2018, 9: 4168.
|
[49] |
BARTEL C J, SUTTON C, GOLDSMITH B R, et al. New tolerance factor to predict the stability of perovskite oxides and halides[J]. Science Advances, 2019, 5(2): evva0693.
|
[50] |
ANDERSEN M, LEVCHENKO S V, SCHEFFLER M, et al. Beyond scaling relations for the description of catalytic materials[J]. ACS Catalysis, 2019, 9(4): 2752-2759.
|
[51] |
OUYANG R H. Exploiting ionic radii for rational design of halide perovskites[J]. Chemistry of Materials,2020, 32(1): 595-604.
|
[52] |
HAYNES W M. CRC Handbook of Chemistry and Physics[M]. Boca Raton, FL: CRC Press, 2014.
|
[53] |
MEDVEDEV V A, COX J D, WAGMAN D D. CODATA Key Values for Thermodynamics[M]. New York: Hemisphere Publishing Corporation, 1989.
|
[54] |
BLUM A L, LANGLEY P. Selection of relevant features and examples in machine learning[J]. Artificial Intelligence, 1997, 97(1/2): 245-271.
|
[55] |
BREIMAN L, SPECTOR P. Submodel selection and evaluation in regression. The X-random case[J]. International Statistical Review, 1992, 60(3): 291-319.
|
[56] |
RAO R B, FUNG G, ROSALES R. On the dangers of cross-validation. An experimental evaluation[C]// Proceedings of the 2008 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2008:588.
|
[57] |
GHIRINGHELLI L M, VYBIRAL J, AHMETCIK E, et al. Learning physical descriptors for materials science by compressed sensing[J]. New Journal of Physics, 2017, 19(2): 23017.
|
[58] |
MA X F, LI Z, ACHENIE L E K, et al. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening[J]. The Journal of Physical Chemistry Letters, 2015, 6(18): 3528-3533.
|
[59] |
吴春峰, 李慧改, 郑少波, 等. 二元合金热力学模型—Miedema模型[J].上海金属, 2011, 33(4): 1-5.
|
[60] |
WATSON R E, BENNETT L H. Optimized predictions for heats of formation of transition-metal alloys II [J]. Calphad, 1984, 8(4): 307-321.)
|