Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

Xinyan Liu, Hong-Jie Peng

PDF(6470 KB)
PDF(6470 KB)
Engineering ›› 2024, Vol. 39 ›› Issue (8) : 25-44. DOI: 10.1016/j.eng.2023.07.021
Research
Review

Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning

Author information +
History +

Abstract

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

Graphical abstract

Keywords

Machine learning / Heterogeneous catalysis / Chemisorption / Theoretical simulation / Materials design / High-throughput screening

Cite this article

Download citation ▾
Xinyan Liu, Hong-Jie Peng. Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning. Engineering, 2024, 39(8): 25‒44 https://doi.org/10.1016/j.eng.2023.07.021

References

[[1]]
C.R. Catlow, M. Davidson, C. Hardacre, G.J. Hutchings. Catalysis making the world a better place. Philos Trans R Soc A Eng Sci, 374 (2061) (2016), p. 20150089.
[[2]]
R. Schlögl. Heterogeneous catalysis. Angew Chem Int Ed Engl, 54 (11) (2015), pp. 3465-3520.
[[3]]
A.Q. Wang, J. Li, T. Zhang. Heterogeneous single-atom catalysis. Nat Rev Chem, 2 (6) (2018), pp. 65-81.
[[4]]
J.R. Rostrup-Nielsen, J. Sehested, J.K. Nørskov. Hydrogen and synthesis gas by steam- and CO2 reforming. Adv Catal, 47 (2002), pp. 65-139.
[[5]]
Q.R. Wang, J.P. Guo, P. Chen. Recent progress towards mild-condition ammonia synthesis. J Energy Chem, 36 (2019), pp. 25-36.
[[6]]
E.T.C. Vogt, B.M. Weckhuysen. Fluid catalytic cracking: recent developments on the grand old lady of zeolite catalysis. Chem Soc Rev, 44 (20) (2015), pp. 7342-7370.
[[7]]
X. Jiang, X. Nie, X. Guo, C. Song, J.G.G. Chen. Recent advances in carbon dioxide hydrogenation to methanol via heterogeneous catalysis. Chem Rev, 120 (15) (2020), pp. 7984-8034.
[[8]]
K. Tomishige, Y. Nakagawa, M. Tamura. Taming heterogeneous rhenium catalysis for the production of biomass-derived chemicals. Chin Chem Lett, 31 (5) (2020), pp. 1071-1077.
[[9]]
P. Schwach, X. Pan, X. Bao. Direct conversion of methane to value-added chemicals over heterogeneous catalysts: challenges and prospects. Chem Rev, 117 (13) (2017), pp. 8497-8520.
[[10]]
Y. Dai, X. Gao, Q. Wang, X. Wan, C. Zhou, Y. Yang. Recent progress in heterogeneous metal and metal oxide catalysts for direct dehydrogenation of ethane and propane. Chem Soc Rev, 50 (9) (2021), pp. 5590-5630.
[[11]]
Z.W. Seh, J. Kibsgaard, C.F. Dickens, I. Chorkendorff, J.K. Nørskov, T.F. Jaramillo. Combining theory and experiment in electrocatalysis: insights into materials design. Science, 355 (6321) (2017), Article eaad4998.
[[12]]
R.M. Bullock, J.G.G. Chen, L. Gagliardi, P.J. Chirik, O.K. Farha, C.H. Hendon, et al. Using nature’s blueprint to expand catalysis with Earth-abundant metals. Science, 369 (6505) (2020), Article eabc3183.
[[13]]
S. Chu, Y. Cui, N. Liu. The path towards sustainable energy. Nat Mater, 16 (1) (2016), pp. 16-22.
[[14]]
P. Nikolaidis, A. Poullikkas. A comparative overview of hydrogen production processes. Renew Sustain Energy Rev, 67 (2017), pp. 597-611.
[[15]]
M.F. Lagadec, A. Grimaud. Water electrolysers with closed and open electrochemical systems. Nat Mater, 19 (11) (2020), pp. 1140-1150.
[[16]]
L. Zhang, Z.J. Zhao, J. Gong. Nanostructured materials for heterogeneous electrocatalytic CO2 reduction and their related reaction mechanisms. Angew Chem Int Ed Engl, 56 (38) (2017), pp. 11326-11353.
[[17]]
D.F. Gao, R.M. Aran-Ais, H.S. Jeon, C.B. Roldan. Rational catalyst and electrolyte design for CO2 electroreduction towards multicarbon products. Nat Catal, 2 (3) (2019), pp. 198-210.
[[18]]
S. Nitopi, E. Bertheussen, S.B. Scott, X. Liu, A.K. Engstfeld, S. Horch, et al. Progress and perspectives of electrochemical CO2 reduction on copper in aqueous electrolyte. Chem Rev, 119 (12) (2019), pp. 7610-7672.
[[19]]
M.B. Ross, P. De Luna, Y.F. Li, C.T. Dinh, D. Kim, P. Yang, et al. Designing materials for electrochemical carbon dioxide recycling. Nat Catal, 2 (8) (2019), pp. 648-658.
[[20]]
X.Y. Liu, B.Q. Li, B. Ni, L. Wang, H.J. Peng. A perspective on the electrocatalytic conversion of carbon dioxide to methanol with metallomacrocyclic catalysts. J Energy Chem, 64 (2022), pp. 263-275.
[[21]]
Z. Zhu, Z. Li, J. Wang, R. Li, H. Chen, Y. Li, et al. Improving NiNx and pyridinic N active sites with space-confined pyrolysis for effective CO2 electroreduction. eScience, 2 (4) (2022), pp. 445-452.
[[22]]
Z.Q. Gao, J.J. Li, Z.C. Zhang, W.P. Hu. Recent advances in carbon-based materials for electrochemical CO2 reduction reaction. Chin Chem Lett, 33 (5) (2022), pp. 2270-2280.
[[23]]
J.G. Chen, R.M. Crooks, L.C. Seefeldt, K.L. Bren, R.M. Bullock, M.Y. Darensbourg, et al. Beyond fossil fuel-driven nitrogen transformations. Science, 360 (6391) (2018), Article eaar6611.
[[24]]
B.H.R. Suryanto, H.L. Du, D.B. Wang, J. Chen, A.N. Simonov, D.R. MacFarlane. Challenges and prospects in the catalysis of electroreduction of nitrogen to ammonia. Nat Catal, 2 (4) (2019), pp. 290-296.
[[25]]
S.Z. Andersen, V. Čolić, S. Yang, J.A. Schwalbe, A.C. Nielander, J.M. McEnaney, et al. A rigorous electrochemical ammonia synthesis protocol with quantitative isotope measurements. Nature, 570 (7762) (2019), pp. 504-508.
[[26]]
X.Y. Cui, C. Tang, Q. Zhang. A review of electrocatalytic reduction of dinitrogen to ammonia under ambient conditions. Adv Energy Mater, 8 (22) (2018), Article 1800369.
[[27]]
Y. Jiao, Y. Zheng, M. Jaroniec, S.Z. Qiao. Design of electrocatalysts for oxygen- and hydrogen-involving energy conversion reactions. Chem Soc Rev, 44 (8) (2015), pp. 2060-2086.
[[28]]
C.C.L. McCrory, S. Jung, I.M. Ferrer, S.M. Chatman, J.C. Peters, T.F. Jaramillo. Benchmarking hydrogen evolving reaction and oxygen evolving reaction electrocatalysts for solar water splitting devices. J Am Chem Soc, 137 (13) (2015), pp. 4347-4357.
[[29]]
M. Shao, Q. Chang, J.P. Dodelet, R. Chenitz. Recent advances in electrocatalysts for oxygen reduction reaction. Chem Rev, 116 (6) (2016), pp. 3594-3657.
[[30]]
J. Kibsgaard, I. Chorkendorff. Considerations for the scaling-up of water splitting catalysts. Nat Energy, 4 (6) (2019), pp. 430-433.
[[31]]
J.K. Nørskov, T. Bligaard, J. Rossmeisl, C.H. Christensen. Towards the computational design of solid catalysts. Nat Chem, 1 (1) (2009), pp. 37-46.
[[32]]
A. Bruix, J.T. Margraf, M. Andersen, K. Reuter. First-principles-based multiscale modelling of heterogeneous catalysis. Nat Catal, 2 (8) (2019), pp. 659-670.
[[33]]
B.W.J. Chen, L. Xu, M. Mavrikakis. Computational methods in heterogeneous catalysis. Chem Rev, 121 (2) (2021), pp. 1007-1048.
[[34]]
A.H. Motagamwala, J.A. Dumesic. Microkinetic modeling: a tool for rational catalyst design. Chem Rev, 121 (2) (2021), pp. 1049-1076.
[[35]]
J. Greeley. Theoretical heterogeneous catalysis: scaling relationships and computational catalyst design. Annu Rev Chem Biomol Eng, 7 (1) (2016), pp. 605-635.
[[36]]
Z.J. Zhao, S.H. Liu, S.J. Zha, D.F. Cheng, F. Studt, G. Henkelman, et al. Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors. Nat Rev Mater, 4 (12) (2019), pp. 792-804.
[[37]]
C.T. Campbell. Energies of adsorbed catalytic intermediates on transition metal surfaces: calorimetric measurements and benchmarks for theory. Acc Chem Res, 52 (4) (2019), pp. 984-993.
[[38]]
A.J. Medford, A. Vojvodic, J.S. Hummelshoj, J. Voss, F. Abild-Pedersen, F. Studt, et al. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis. J Catal, 328 (2015), pp. 36-42.
[[39]]
K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh. Machine learning for molecular and materials science. Nature, 559 (7715) (2018), pp. 547-555.
[[40]]
V. Tshitoyan, J. Dagdelen, L. Weston, A. Dunn, Z. Rong, O. Kononova, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571 (7763) (2019), pp. 95-98.
[[41]]
T. Zhou, Z. Song, K. Sundmacher. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering, 5 (6) (2019), pp. 1017-1026.
[[42]]
A. Chen, X. Zhang, Z. Zhou. Machine learning: accelerating materials development for energy storage and conversion. InfoMat, 2 (3) (2020), pp. 553-576.
[[43]]
Y. Liu, B.R. Guo, X.X. Zou, Y.J. Li, S.Q. Shi. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater, 31 (2020), pp. 434-450.
[[44]]
X. Chen, X. Liu, X. Shen, Q. Zhang. Applying machine learning to rechargeable batteries: from the microscale to the macroscale. Angew Chem Int Ed, 60 (46) (2021), pp. 24354-24366.
[[45]]
J.Z. Li, X.B. Huang, P. Pianetta, Y.J. Liu. Machine-and-data intelligence for synchrotron science. Nat Rev Phys, 3 (12) (2021), pp. 766-768.
[[46]]
S. Xu, J. Li, P. Cai, X. Liu, B. Liu, X. Wang. Self-improving photosensitizer discovery system via Bayesian search with first-principle simulations. J Am Chem Soc, 143 (47) (2021), pp. 19769-19777.
[[47]]
S.N. Li, Y.J. Liu, D. Chen, Y. Jiang, Z.W. Nie, F. Pan. Encoding the atomic structure for machine learning in materials science. Wiley Interdiscip Rev Comput Mol Sci, 12 (1) (2022), p. e1558.
[[48]]
T. Lombardo, M. Duquesnoy, H. El-Bouysidy, F. Årén, A. Gallo-Bueno, P.B. Jørgensen, et al. Artificial intelligence applied to battery research: hype or reality>. Chem Rev, 122 (12) (2022), pp. 10899-10969.
[[49]]
X.Y. Liu, X.Q. Zhang, X. Chen, G.L. Zhu, C. Yan, J.Q. Huang, et al. A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries. J Energy Chem, 68 (2022), pp. 548-555.
[[50]]
M. Lin, J. Xiong, M. Su, F. Wang, X. Liu, Y. Hou, et al. A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials. Chem Sci, 13 (26) (2022), pp. 7863-7872.
[[51]]
X. Wang, S. Jiang, W. Hu, S. Ye, T. Wang, F. Wu, et al. Quantitatively determining surface-adsorbate properties from vibrational spectroscopy with interpretable machine learning. J Am Chem Soc, 144 (35) (2022), pp. 16069-16076.
[[52]]
J.C.A. Oliveira, J. Frey, S.Q. Zhang, L.C. Xu, X. Li, S.W. Li, et al. When machine learning meets molecular synthesis. Trends Chem, 4 (10) (2022), pp. 863-885.
[[53]]
X. Liu, H.J. Peng, B.Q. Li, X. Chen, Z. Li, J.Q. Huang, et al. Untangling degradation chemistries of lithium-sulfur batteries through interpretable hybrid machine learning. Angew Chem Int Ed Engl, 61 (48) (2022), p. e202214037.
[[54]]
Z.P. Yao, Y.W. Lum, A. Johnston, L.M. Mejia-Mendoza, X. Zhou, Y.G. Wen, et al. Machine learning for a sustainable energy future. Nat Rev Mater, 8 (3) (2022), pp. 202-215.
[[55]]
J.A. Esterhuizen, B.R. Goldsmith, S. Linic. Interpretable machine learning for knowledge generation in heterogeneous catalysis. Nat Catal, 5 (3) (2022), pp. 175-184.
[[56]]
A.J. Medford, M.R. Kunz, S.M. Ewing, T. Borders, R. Fushimi. Extracting knowledge from data through catalysis informatics. ACS Catal, 8 (8) (2018), pp. 7403-7429.
[[57]]
P.S. Lamoureux, K.T. Winther, J.A.G. Torres, V. Streibel, M. Zhao, M. Bajdich, et al. Machine learning for computational heterogeneous catalysis. ChemCatChem, 11 (16) (2019), pp. 3581-3601.
[[58]]
T. Toyao, Z. Maeno, S. Takakusagi, T. Kamachi, I. Takigawa, K. Shimizu. Machine learning for catalysis informatics: recent applications and prospects. ACS Catal, 10 (3) (2020), pp. 2260-2297.
[[59]]
G.H. Gu, C. Choi, Y. Lee, A.B. Situmorang, J. Noh, Y.H. Kim, et al. Progress in computational and machine-learning methods for heterogeneous small-molecule activation. Adv Mater, 32 (35) (2020), p. 1907865.
[[60]]
S.C. Ma, Z.P. Liu. Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future. ACS Catal, 10 (22) (2020), pp. 13213-13226.
[[61]]
J. Xu, X.M. Cao, P. Hu. Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis. Phys Chem Chem Phys, 23 (19) (2021), pp. 11155-11179.
[[62]]
L.T. Chen, X. Zhang, A. Chen, S. Yao, X. Hu, Z. Zhou. Targeted design of advanced electrocatalysts by machine learning. Chin J Catal, 43 (1) (2022), pp. 11-32.
[[63]]
L. Cao. Recent advances in the application of machine-learning algorithms to predict adsorption energies. Trends Chem, 4 (4) (2022), pp. 347-360.
[[64]]
H. Li, Y. Jiao, K. Davey, S.Z. Qiao. Data-driven machine learning for understanding surface structures of heterogeneous catalysts. Angew Chem Int Ed, 62 (9) (2023), Article e202216383.
[[65]]
T.Y. Mou, H.S. Pillai, S.W. Wang, M.Y. Wan, X. Han, N.M. Schweitzer, et al. Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal, 6 (2) (2023), pp. 122-136.
[[66]]
H. Yang, Z.Q. He, M.D. Zhang, X.J. Tan, K. Sun, H.Y. Liu, et al. Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning. EcoMat, 5 (5) (2023), p. e12330.
[[67]]
B. Hammer, J.K. Nørskov. Why gold is the noblest of all the metals. Nature, 376 (6537) (1995), pp. 238-240.
[[68]]
J.K. Nørskov, F. Studt, F. Abild-Pedersen, T. Bligaard. Fundamental concepts in heterogeneous catalysis. John Wiley & Sons, Inc., Hoboken (2014).
[[69]]
F. Abild-Pedersen, J. Greeley, F. Studt, J. Rossmeisl, T.R. Munter, P.G. Moses, et al. Scaling properties of adsorption energies for hydrogen-containing molecules on transition-metal surfaces. Phys Rev Lett, 99 (1) (2007), Article 016105.
[[70]]
A.J. Chowdhury, W.Q. Yang, E. Walker, O. Mamun, A. Heyden, G.A. Terejanu. Prediction of adsorption energies for chemical species on metal catalyst surfaces using machine learning. J Phys Chem C, 122 (49) (2018), pp. 28142-28150.
[[71]]
I.C. Man, H.Y. Su, F. Calle-Vallejo, H.A. Hansen, J.I. Martinez, N.G. Inoglu, et al. Universality in oxygen evolution electrocatalysis on oxide surfaces. ChemCatChem, 3 (7) (2011), pp. 1159-1165.
[[72]]
A.A. Latimer, A.R. Kulkarni, H. Aljama, J.H. Montoya, J.S. Yoo, C. Tsai, et al. Understanding trends in C-H bond activation in heterogeneous catalysis. Nat Mater, 16 (2) (2017), pp. 225-229.
[[73]]
T. Wang, X.J. Cui, K.T. Winther, F. Abild-Pedersen, T. Bligaard, J.K. Nørskov. Theory-aided discovery of metallic catalysts for selective propane dehydrogenation to propylene. ACS Catal, 11 (10) (2021), pp. 6290-6297.
[[74]]
O. Mamun, K.T. Winther, J.R. Boes, T. Bligaard. A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts. npj Comput Mater, 6 (1) (2020), p. 177.
[[75]]
R. García-Muelas, N. López. Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals. Nat Commun, 10 (1) (2019), p. 4687.
[[76]]
T. Bligaard, J.K. Nørskov, S. Dahl, J. Matthiesen, C.H. Christensen, J. Sehested. The Bronsted-Evans-Polanyi relation and the volcano curve in heterogeneous catalysis. J Catal, 224 (1) (2004), pp. 206-217.
[[77]]
L. Yu, F. Abild-Pedersen. Bond order conservation strategies in catalysis applied to the NH3 decomposition reaction. ACS Catal, 7 (1) (2017), pp. 864-871.
[[78]]
H.J. Peng, M.T. Tang, X.Y. Liu, P. Schlexer Lamoureux, M. Bajdich, F. Abild-Pedersen. The role of atomic carbon in directing electrochemical CO2 reduction to multicarbon products. Energy Environ Sci, 14 (1) (2021), pp. 473-482.
[[79]]
Y.L. Cheng, C.T. Hsieh, Y.S. Ho, M.H. Shen, T.H. Chao, M.J. Cheng. Examination of the Brønsted-Evans-Polanyi relationship for the hydrogen evolution reaction on transition metals based on constant electrode potential density functional theory. Phys Chem Chem Phys, 24 (4) (2022), pp. 2476-2481.
[[80]]
J.S. Hummelshøj, F. Abild-Pedersen, F. Studt, T. Bligaard, J.K. Nørskov. CatApp: a web application for surface chemistry and heterogeneous catalysis. Angew Chem Int Ed Engl, 51 (1) (2012), pp. 272-274.
[[81]]
K. Takahashi, I. Miyazato. Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning. J Comput Chem, 39 (28) (2018), pp. 2405-2408.
[[82]]
N. Artrith, Z.X. Lin, J.G. Chen. Predicting the activity and selectivity of bimetallic metal catalysts for ethanol reforming using machine learning. ACS Catal, 10 (16) (2020), pp. 9438-9444.
[[83]]
X. Ma, Z. Li, L.E.K. Achenie, H. Xin. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening. J Phys Chem Lett, 6 (18) (2015), pp. 3528-3533.
[[84]]
Z. Li, S.W. Wang, W.S. Chin, L.E. Achenie, H.L. Xin. High-throughput screening of bimetallic catalysts enabled by machine learning. J Mater Chem A, 5 (46) (2017), pp. 24131-24138.
[[85]]
C.S. Praveen, A. Comas-Vives. Design of an accurate machine learning algorithm to predict the binding energies of several adsorbates on multiple sites of metal surfaces. ChemCatChem, 12 (18) (2020), pp. 4611-4617.
[[86]]
S. Wang, H.S. Pillai, H. Xin. Bayesian learning of chemisorption for bridging the complexity of electronic descriptors. Nat Commun, 11 (1) (2020), p. 6132.
[[87]]
F. Göltl, P. Muller, P. Uchupalanun, P. Sautet, I. Hermans. Developing a descriptor-based approach for CO and NO adsorption strength to transition metal sites in zeolites. Chem Mater, 29 (15) (2017), pp. 6434-6444.
[[88]]
C. Liu, Y.X. Li, M. Takao, T. Toyao, Z. Maeno, T. Kamachi, et al. Frontier molecular orbital based analysis of solid-adsorbate interactions over group 13 metal oxide surfaces. J Phys Chem C, 124 (28) (2020), pp. 15355-15365.
[[89]]
M.V. Jyothirmai, D. Roshini, B.M. Abraham, J.K. Singh. Accelerating the discovery of g-C3N4-supported single atom catalysts for hydrogen evolution reaction: a combined DFT and machine learning strategy. ACS Appl Energy Mater, 6 (10) (2023), pp. 5598-5606.
[[90]]
T.Y. Liu, X. Zhao, X.F. Liu, W.J. Xiao, Z.J. Luo, W.T. Wang, et al. Understanding the hydrogen evolution reaction activity of doped single-atom catalysts on two-dimensional GaPS4 by DFT and machine learning. J Energy Chem, 81 (2023), pp. 93-100.
[[91]]
H. Sun, Y.Z. Li, L.Y. Gao, M.Y. Chang, X.R. Jin, B.Y. Li, et al. High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning. J Energy Chem, 81 (2023), pp. 349-357.
[[92]]
A. Chen, X. Zhang, L.T. Chen, S. Yao, Z. Zhou. A machine learning model on simple features for CO2 reduction electrocatalysts. J Phys Chem C, 124 (41) (2020), pp. 22471-22478.
[[93]]
M. Andersen, S.V. Levchenko, M. Scheffler, K. Reuter. Beyond scaling relations for the description of catalytic materials. ACS Catal, 9 (4) (2019), pp. 2752-2759.
[[94]]
V. Fung, G. Hu, P. Ganesh, B.G. Sumpter. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun, 12 (1) (2021), p. 88.
[[95]]
J.A. Esterhuizen, B.R. Goldsmith, S. Linic. Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning. Chem Catal, 1 (4) (2021), pp. 923-940.
[[96]]
T. Toyao, K. Suzuki, S. Kikuchi, S. Takakusagi, K. Shimizu, I. Takigawa. Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys. J Phys Chem C, 122 (15) (2018), pp. 8315-8326.
[[97]]
J. Noh, S. Back, J. Kim, Y. Jung. Active learning with non-ab initio input features toward efficient CO2 reduction catalysts. Chem Sci, 9 (23) (2018), pp. 5152-5159. View article.
[[98]]
J.A. Esterhuizen, B.R. Goldsmith, S. Linic. Theory-guided machine learning finds geometric structure-property relationships for chemisorption on subsurface alloys. Chem, 6 (11) (2020), pp. 3100-3117.
[[99]]
T.R. Wang, J.C. Li, W. Shu, S.L. Hu, R.H. Ouyang, W.X. Li. Machine-learning adsorption on binary alloy surfaces for catalyst screening. Chin J Chem Phys, 33 (6) (2020), pp. 703-711.
[[100]]
X. Zhang, Z. Wang, A.M. Lawan, J.H. Wang, C.Y. Hsieh, C.R. Duan, et al. Data-driven structural descriptor for predicting platinum-based alloys as oxygen reduction electrocatalysts. InfoMat, 5 (6) (2023), p. e12406.
[[101]]
M.M. Montemore, J.W. Medlin. A unified picture of adsorption on transition metals through different atoms. J Am Chem Soc, 136 (26) (2014), pp. 9272-9275.
[[102]]
M.M. Montemore, C.F. Nwaokorie, G.O. Kayode. General screening of surface alloys for catalysis. Catal Sci Technol, 10 (13) (2020), pp. 4467-4476.
[[103]]
G.A. Somorjai, J.Y. Park. Molecular surface chemistry by metal single crystals and nanoparticles from vacuum to high pressure. Chem Soc Rev, 37 (10) (2008), pp. 2155-2162.
[[104]]
J.K. Nørskov, T. Bligaard, B. Hvolbaek, F. Abild-Pedersen, I. Chorkendorff, C.H. Christensen. The nature of the active site in heterogeneous metal catalysis. Chem Soc Rev, 37 (10) (2008), pp. 2163-2171.
[[105]]
F. Calle-Vallejo, D. Loffreda, M.T.M. Koper, P. Sautet. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat Chem, 7 (5) (2015), pp. 403-410.
[[106]]
F. Calle-Vallejo, J. Tymoczko, V. Colic, Q.H. Vu, M.D. Pohl, K. Morgenstern, et al. Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors. Science, 350 (6257) (2015), pp. 185-189.
[[107]]
X. Liu, J. Xiao, H. Peng, X. Hong, K. Chan, J.K. Nørskov. Understanding trends in electrochemical carbon dioxide reduction rates. Nat Commun, 8 (1) (2017), p. 15438.
[[108]]
T.S. Choksi, L.T. Roling, V. Streibel, F. Abild-Pedersen. Predicting adsorption properties of catalytic descriptors on bimetallic nanoalloys with site-specific precision. J Phys Chem Lett, 10 (8) (2019), pp. 1852-1859.
[[109]]
R.A. Sheldon. Green and sustainable manufacture of chemicals from biomass: state of the art. Green Chem, 16 (3) (2014), pp. 950-963.
[[110]]
C. Mondelli, G. Gözaydın, N. Yan, J. Pérez-Ramírez. Biomass valorisation over metal-based solid catalysts from nanoparticles to single atoms. Chem Soc Rev, 49 (12) (2020), pp. 3764-3782.
[[111]]
I. Vollmer, M.J.F. Jenks, M.C.P. Roelands, R.J. White, T. Van Harmelen, P. de Wild, et al. Beyond mechanical recycling: giving new life to plastic waste. Angew Chem Int Ed Engl, 59 (36) (2020), pp. 15402-15423.
[[112]]
H. Zhou, Y. Wang, Y. Ren, Z.H. Li, X.G. Kong, M.F. Shao, et al. Plastic waste valorization by leveraging multidisciplinary catalytic technologies. ACS Catal, 12 (15) (2022), pp. 9307-9324.
[[113]]
R.A. Hoyt, M.M. Montemore, I. Fampiou, W. Chen, G. Tritsaris, E. Kaxiras. Machine learning prediction of H adsorption energies on Ag alloys. J Chem Inf Model, 59 (4) (2019), pp. 1357-1365.
[[114]]
S. Saxena, T.S. Khan, F. Jalid, M. Ramteke, M.A. Haider. In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling. J Mater Chem A, 8 (1) (2020), pp. 107-123.
[[115]]
X.Y. Liu, C. Cai, W.H. Zhao, H.J. Peng, T. Wang. Machine learning-assisted screening of stepped alloy surfaces for C1 catalysis. ACS Catal, 12 (8) (2022), pp. 4252-4260.
[[116]]
Z. Yang, W. Gao, Q. Jiang. A machine learning scheme for the catalytic activity of alloys with intrinsic descriptors. J Mater Chem A, 8 (34) (2020), pp. 17507-17515.
[[117]]
X. Zong, D.G. Vlachos. Exploring structure-sensitive relations for small species adsorption using machine learning. J Chem Inf Model, 62 (18) (2022), pp. 4361-4368.
[[118]]
J. Yang, Z. Wang, Z. Liu, Q. Wang, Y. Wen, A. Zhang, et al. Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning. J Mater Chem A, 10 (47) (2022), pp. 25238-25248.
[[119]]
T.A.A. Batchelor, J.K. Pedersen, S.H. Winther, I.E. Castelli, K.W. Jacobsen, J. Rossmeisl. High-entropy alloys as a discovery platform for electrocatalysis. Joule, 3 (3) (2019), pp. 834-845.
[[120]]
D. Roy, S.C. Mandal, B. Pathak. Machine learning-driven high-throughput screening of alloy-based catalysts for selective CO2 hydrogenation to methanol. ACS Appl Mater Interfaces, 13 (47) (2021), pp. 56151-56163.
[[121]]
N.K. Pandit, D. Roy, S.C. Mandal, B. Pathak. Rational designing of bimetallic/trimetallic hydrogen evolution reaction catalysts using supervised machine learning. J Phys Chem Lett, 13 (32) (2022), pp. 7583-7593.
[[122]]
X. Zhang, K.P. Li, B. Wen, J. Ma, D.F. Diao. Machine learning accelerated DFT research on platinum-modified amorphous alloy surface catalysts. Chin Chem Lett, 34 (5) (2023), Article 107833.
[[123]]
K. Tran, Z.W. Ulissi. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal, 1 (9) (2018), pp. 696-703.
[[124]]
T. Xie, J.C. Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett, 120 (14) (2018), Article 145301.
[[125]]
S. Back, J. Yoon, N. Tian, W. Zhong, K. Tran, Z.W. Ulissi. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J Phys Chem Lett, 10 (15) (2019), pp. 4401-4408.
[[126]]
G.H. Gu, J. Noh, S. Kim, S. Back, Z. Ulissi, Y. Jung. Practical deep-learning representation for fast heterogeneous catalyst screening. J Phys Chem Lett, 11 (9) (2020), pp. 3185-3191.
[[127]]
A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater, 1 (1) (2013), Article 011002.
[[128]]
S.H. Wang, H.S. Pillai, S. Wang, L.E.K. Achenie, H. Xin. Infusing theory into deep learning for interpretable reactivity prediction. Nat Commun, 12 (1) (2021), p. 5288.
[[129]]
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput, 9 (8) (2013), pp. 3404-3419.
[[130]]
D. Rogers, M. Hahn. Extended-connectivity fingerprints. J Chem Inf Model, 50 (5) (2010), pp. 742-754.
[[131]]
B. Huang, O.A. Von Lilienfeld. Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nat Chem, 12 (10) (2020), pp. 945-951.
[[132]]
K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O.A. Von Lilienfeld, K.R. Müller, et al. Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J Phys Chem Lett, 6 (12) (2015), pp. 2326-2331.
[[133]]
A.S. Christensen, L.A. Bratholm, F.A. Faber,OA Von Lilienfeld. FCHL revisited: faster and more accurate quantum machine learning. J Chem Phys, 152 (4) (2020), p. 044107.
[[134]]
X. Li, R. Chiong, Z. Hu, D. Cornforth, A.J. Page. Improved representations of heterogeneous carbon reforming catalysis using machine learning. J Chem Theory Comput, 15 (12) (2019), pp. 6882-6894.
[[135]]
X. Li, R. Chiong, A.J. Page. Group and period-based representations for improved machine learning prediction of heterogeneous alloy catalysts. J Phys Chem Lett, 12 (21) (2021), pp. 5156-5162.
[[136]]
D. SMILES Weininger. a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci, 28 (1) (1988), pp. 31-36.
[[137]]
A.J. Chowdhury, W. Yang, K.E. Abdelfatah, M. Zare, A. Heyden, G.A. Terejanu. A multiple filter based neural network approach to the extrapolation of adsorption energies on metal surfaces for catalysis applications. J Chem Theory Comput, 16 (2) (2020), pp. 1105-1114.
[[138]]
A.J. Chowdhury, W.Q. Yang, A. Heyden, G.A. Terejanu. Comparative study on the machine learning-based prediction of adsorption energies for ring and chain species on metal catalyst surfaces. J Phys Chem C, 125 (32) (2021), pp. 17742-17748.
[[139]]
B.C. Wang, T.J. Gu, Y.J. Lu, B. Yang. Prediction of energies for reaction intermediates and transition states on catalyst surfaces using graph-based machine learning models. Mol Catal, 498 (2020), Article 111266.
[[140]]
S. Pablo-García, S. Morandi, R.A. Vargas-Hernández, K. Jorner, Z. Ivković, N. López, et al. Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks. Nat Comput Sci, 3 (5) (2023), pp. 433-442.
[[141]]
R. Jinnouchi, R. Asahi. Predicting catalytic activity of nanoparticles by a DFT-aided machine-learning algorithm. J Phys Chem Lett, 8 (17) (2017), pp. 4279-4283.
[[142]]
M.O.J. Jager, E.V. Morooka, F.F. Canova, L. Himanen, A.S. Foster. Machine learning hydrogen adsorption on nanoclusters through structural descriptors. npj Comput Mater, 4 (2018), p. 37.
[[143]]
Y. Chen, Y. Huang, T. Cheng, W.A. Goddard III. Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J Am Chem Soc, 141 (29) (2019), pp. 11651-11657.
[[144]]
A.C.T. Van Duin, S. Dasgupta, F. Lorant, W.A. Goddard. ReaxFF: a reactive force field for hydrocarbons. J Phys Chem A, 105 (41) (2001), pp. 9396-9409.
[[145]]
S. Naserifar, Y.L. Chen, S. Kwon, H. Xiao, W.A. Goddard III. Artificial intelligence and QM/MM with a polarizable reactive force field for next-generation electrocatalysts. Matter, 4 (1) (2021), pp. 195-216.
[[146]]
K. Jiang, Y.F. Huang, G.S. Zeng, F.M. Toma, W.A. Goddard III, A.T. Bell. Effects of surface roughness on the electrochemical reduction of CO2 over Cu. ACS Energy Lett, 5 (4) (2020), pp. 1206-1214.
[[147]]
G.H. Gu, J. Lim, C. Wan, T. Cheng, H. Pu, S. Kim, et al. Autobifunctional mechanism of jagged Pt nanowires for hydrogen evolution kinetics via end-to-end simulation. J Am Chem Soc, 143 (14) (2021), pp. 5355-5363.
[[148]]
J.W. Zhang, P.J. Hu, H.F. Wang. Amorphous catalysis: machine learning driven high-throughput screening of superior active site for hydrogen evolution reaction. J Phys Chem C, 124 (19) (2020), pp. 10483-10494.
[[149]]
P.G. Ghanekar, S. Deshpande, J. Greeley. Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis. Nat Commun, 13 (1) (2022), p. 5788.
[[150]]
S. Deshpande, T. Maxson, J. Greeley. Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis. npj Comput Mater, 6 (1) (2020), p. 79.
[[151]]
L. Cao, T. Mueller. Catalytic activity maps for alloy nanoparticles. J Am Chem Soc, 145 (13) (2023), pp. 7352-7360.
[[152]]
M. Zhong, K. Tran, Y. Min, C. Wang, Z. Wang, C.T. Dinh, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature, 581 (7807) (2020), pp. 178-183.
[[153]]
L. Van der Maaten. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res, 15 (1) (2014), pp. 3221-3245.
[[154]]
H.S. Pillai, Y. Li, S.H. Wang, N. Omidvar, Q. Mu, L.E.K. Achenie, et al. Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks. Nat Commun, 14 (1) (2023), p. 792.
[[155]]
S. Zhai, H.P. Xie, P. Cui, D.Q. Guan, J. Wang, S.Y. Zhao, et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells. Nat Energy, 7 (9) (2022), pp. 866-875.
[[156]]
Q. Gao, H.S. Pillai, Y. Huang, S. Liu, Q. Mu, X. Han, et al. Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights. Nat Commun, 13 (1) (2022), p. 2338.
[[157]]
K.T. Winther, M.J. Hoffmann, J.R. Boes, O. Mamun, M. Bajdich, T. Bligaard. Catalysis-Hub. org, an open electronic structure database for surface reactions. Sci Data, 6 (1) (2019), p. 75.
[[158]]
L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, et al. Open catalyst 2020 (OC20) dataset and community challenges. ACS Catal, 11 (10) (2021), pp. 6059-6072.
[[159]]
A. Kolluru, M. Shuaibi, A. Palizhati, N. Shoghi, A. Das, B. Wood, et al. Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery. ACS Catal, 12 (14) (2022), pp. 8572-8581.
[[160]]
C. Chen, W.K. Ye, Y.X. Zuo, C. Zheng, S.P. Ong. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater, 31 (9) (2019), pp. 3564-3572.
[[161]]
N. Yang, A.J. Medford, X. Liu, F. Studt, T. Bligaard, S.F. Bent, et al. Intrinsic selectivity and structure sensitivity of rhodium catalysts for C2+ oxygenate production. J Am Chem Soc, 138 (11) (2016), pp. 3705-3714.
[[162]]
R. Sundararaman, D. Vigil-Fowler, K. Schwarz. Improving the accuracy of atomistic simulations of the electrochemical interface. Chem Rev, 122 (12) (2022), pp. 10651-10674.
[[163]]
X. Liu, P. Schlexer, J. Xiao, Y. Ji, L. Wang, R.B. Sandberg, et al. pH effects on the electrochemical reduction of CO2 towards C2 products on stepped copper. Nat Commun, 10 (1) (2019), p. 32.
[[164]]
H.J. Peng, M.T. Tang, J. Halldin Stenlid, X. Liu, F. Abild-Pedersen. Trends in oxygenate/hydrocarbon selectivity for electrochemical CO2 reduction to C2 products. Nat Commun, 13 (1) (2022), p. 1399.
[[165]]
F.A. Faber, L. Hutchison, B. Huang, J. Gilmer, S.S. Schoenholz, G.E. Dahl, et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theory Comput, 13 (11) (2017), pp. 5255-5264.
[[166]]
M. Bogojeski, L. Vogt-Maranto, M.E. Tuckerman, K.R. Müller, K. Burke. Quantum chemical accuracy from density functional approximations via machine learning. Nat Commun, 11 (1) (2020), p. 5223.
[[167]]
M.K. Bisbo, B. Hammer. Efficient global structure optimization with a machine-learned surrogate model. Phys Rev Lett, 124 (8) (2020), Article 086102.
[[168]]
J. Behler. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angew Chem Int Ed Engl, 56 (42) (2017), pp. 12828-12840.
[[169]]
P. Friederich, F. Häse, J. Proppe, A. Aspuru-Guzik. Machine-learned potentials for next-generation matter simulations. Nat Mater, 20 (6) (2021), pp. 750-761.
AI Summary AI Mindmap
PDF(6470 KB)

Accesses

Citations

Detail

Sections
Recommended

/