Unfolding the structure-property relationships of Li2S anchoring on two-dimensional materials with high-throughput calculations and machine learning

Jin, Lujie; Wang, Hongshuai; Zhao, Hao; Ji, Yujin; Li, Youyong

doi:10.1016/j.jechem.2023.03.004

Cited by 8 publications

(3 citation statements)

References 83 publications

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“…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...…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Introductionmentioning

confidence: 99%

When Machine Learning Meets 2D Materials: A Review

Lu,

Xia,

Ren

et al. 2024

Advanced Science

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“…Leveraging the wealth of data from dedicated platforms, recent advancements in machine learning (ML) are now paving the way for innovative approaches in predicting fundamental properties and uncovering novel 2D materials. 34,37,67–72…”

Section: Introductionmentioning

confidence: 99%

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

Yang,

Kang,

Kim

et al. 2024

Digital Discovery

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Regulation of sulfur molecules for advanced lithium–sulfur batteries: strategies, mechanisms, and characterizations

Wang,

Zhang

2024

Surf. Sci. Tech.

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Lithium–sulfur (Li–S) batteries have been regarded as the pinnacle in the domain of high-energy-density Li–metal batteries, mainly because of their high theoretical specific capacity and natural abundance. However, their practical implementation is chiefly impeded by the sluggish redox kinetics of lithium polysulfides (LiPSs) and the parasitic shuttle effect, which are associated with the intrinsic physiochemical properties of multiphase sulfur species. On this account, rationally regulating the properties of sulfur species at the molecular level is promising to achieve ample opportunities to circumvent these key stumbling blocks, hence driving the practical application of Li–S technology. Herein, the recent achievements in tailoring the molecular structures of sulfur species are summarized and reviewed, including low-order sulfur molecules, heteroatom-doped sulfur molecules, and LiPSs-based functional intermediates. Moreover, some advanced characterizations allowing structural and chemical environment detection of regulated sulfur species, such as X-ray absorption spectroscopy, X-ray emission spectroscopy, neutron scattering, and pair distribution function, are also discussed, aimed at propelling the theoretical and practical research of sulfur species. Finally, future perspectives on the molecular engineering of sulfur species are provided to enlighten the development of advanced Li–S batteries.

show abstract

Unfolding the structure-property relationships of Li2S anchoring on two-dimensional materials with high-throughput calculations and machine learning

Cited by 8 publications

References 83 publications

When Machine Learning Meets 2D Materials: A Review

When Machine Learning Meets 2D Materials: A Review

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

Regulation of sulfur molecules for advanced lithium–sulfur batteries: strategies, mechanisms, and characterizations

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