High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap

Hu, Wenguang; Zhang, Lei

doi:10.1016/j.mtcomm.2023.105841

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…The introduction of ML to research on 2D materials has considerably increased the efficiency of discovering new 2D materials. As shown in Table 3 , most of these new materials are catalytic materials, [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] photoelectric materials, [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ] and ferromagnetic materials. [ 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 90 , 91 , 92 ] In most cases, we search for 2D materials with specific desired properties in existing open‐source databases or in new material sets generated through methods such as element replacement within the original cell.…”

Section: Discovering New 2d Materialsmentioning

confidence: 99%

“…As shown in Table 3, most of these new materials are catalytic materials, [60][61][62][63][64][65][66][67][68] photoelectric materials, [69][70][71][72][73][74][75][76][77] and ferromagnetic materials. [78][79][80][81][82][83][84][85][90][91][92] In most cases, we search for 2D materials with specific desired properties in existing open-source databases or in new material sets generated through methods such as element replacement within the original cell.…”

Section: Discovering New 2d Materialsmentioning

confidence: 99%

“…In studies related to material properties, ML has been combined with DFT and MD to explore the thermal properties, [ 17 , 33 , 34 , 35 , 36 , 37 , 38 , 46 ] bandgaps, [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ] and mechanical properties [ 56 , 57 , 58 , 59 ] of various materials, thereby accelerating the pace of research in this field. Furthermore, ML has also been utilized for the discovery of novel 2D materials, including catalytic, [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] photoelectric, [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ] and magnetic materials. [ 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 90 , 91 , 92 ] In terms of preparing 2D materials, ML methods have been ap...…”

Section: Introductionmentioning

confidence: 99%

“…In studies related to material properties, ML has been combined with DFT and MD to explore the thermal properties, [17,[33][34][35][36][37][38]46] bandgaps, [47][48][49][50][51][52][53][54][55] and mechanical properties [56][57][58][59] of various materials, thereby accelerating the pace of research in this field. Furthermore, ML has also been utilized for the discovery of novel 2D materials, including catalytic, [60][61][62][63][64][65][66][67][68] photoelectric, [69][70][71][72][73][74][75][76][77] and magnetic materials. [78][79][80][81][82][83][84][85][90][91...…”

Section: Introductionmentioning

confidence: 99%

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When Machine Learning Meets 2D Materials: A Review

Lu,

Xia,

Ren

et al. 2024

Advanced Science

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The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

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Section: Discovering New 2d Materialsmentioning

confidence: 99%

Section: Discovering New 2d Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

When Machine Learning Meets 2D Materials: A Review

Lu,

Xia,

Ren

et al. 2024

Advanced Science

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“…The formation energy represents the stability of 4d-TM N @C 54 N 71 and 4d-TM int @C 54 N 72 , and smaller formation energy is desired for stability, making them even more likely to form. 34,35 The results show that 0 and +1 are stable charge states for most 4d-TM N @C 54 N 71 and 4d-TM int @C 54 N 72 . In addition, for Zr N @ C 54 N 71 , Nb N @C 54 N 71 , Ag N @C 54 N 71 , Zr int @C 54 N 72 , Nb int @ C 54 N 72 , Mo int @C 54 N 72 , Ru int @C 54 N 72 , Pd int @C 54 N 72, and Ag int @C 54 N 72 , the +2 charge state is also stable.…”

Section: Formation Energy As a Function Of The Fermimentioning

confidence: 99%

Theoretical Insights on the Charge State and Bifunctional OER/ORR Electrocatalyst Activity in 4d-Transition-Metal-Doped g-C₃N₄ Monolayers

Li,

Zhang,

Feng

et al. 2024

ACS Appl. Mater. Interfaces

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Exploring efficient and stable electrocatalysts for the bifunctional oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is vital to developing renewable energy technologies. However, due to the substantial and intricate design space associated with these bifunctional OER/ORR electrocatalysts, their development presents a formidable challenge, resulting in their cost-prohibitive nature in both experimental and computational studies. Herein, using the defect physics method, we systematically investigate the formation energies and bifunctional overpotential (η Bi ) of 4d-transition-metal (4d-TM, 4d-TM = Zr, Nb, Mo, Ru, Rh, Pd, and Ag)-doped monolayer supercell g-C 3 N 4 (4d-TM@C 54 N 72 ) based on the density functional theory (DFT) calculations. Under N-rich and Crich conditions, we find that the formation energies of Rh N @C 54 N 71 (Rh occupation N) and Pd N @C 54 N 71 (Pd occupation N) are smaller than that of other 4d-TM N @C 54 N 71 (4d-TM occupation N site); for the 4d-TM int @C 54 N 72 (4d-TM interstitial site occupation), the lowest-formation energy defects are Pd int @C 54 N 72 . These results indicate that they have better stabilities. Interestingly, for these formation energy lower systems, Pd 0 int @C 54 N 72 (η Bi = 1.00 V) and Rh 1+ N @C 54 N 71 (η Bi = 0.73 V) have ultralow overpotential and can be great candidates for bifunctional OER/ORR electrocatalysts. We find the reason is that adjusting the charge states of 4d-TM@C 54 N 72 can tune the interaction strength between the oxygenated intermediates and the 4d-TM@ C 54 N 72 , which plays a crucial role in the activity of reactions. Additionally, the data obtained through machine learning (ML) application suggest that the electronegativity (N m ) and bond length of 4d-TM and coordination atoms (d TM-OOH ) are primary descriptors characterizing the OER and ORR activities, respectively. The charged defect tuning of the bifunctional OER/ORR activity for 4d-TM@C 54 N 72 would enable electrocatalytic performance optimization and the development of potential electrocatalysts for renewable energy applications.

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Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

Touati,

Benghia,

Hebboul

et al. 2024

Neural Comput & Applic

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High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap

Cited by 6 publications

References 49 publications

When Machine Learning Meets 2D Materials: A Review

When Machine Learning Meets 2D Materials: A Review

Theoretical Insights on the Charge State and Bifunctional OER/ORR Electrocatalyst Activity in 4d-Transition-Metal-Doped g-C₃N₄ Monolayers

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

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High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap

Cited by 6 publications

References 49 publications

When Machine Learning Meets 2D Materials: A Review

When Machine Learning Meets 2D Materials: A Review

Theoretical Insights on the Charge State and Bifunctional OER/ORR Electrocatalyst Activity in 4d-Transition-Metal-Doped g-C3N4 Monolayers

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

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Product

Resources

About

Theoretical Insights on the Charge State and Bifunctional OER/ORR Electrocatalyst Activity in 4d-Transition-Metal-Doped g-C₃N₄ Monolayers