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Molecular dynamics (MD) simulation is an extremely useful tool in battery research as it provides critical information for improving battery performance and developing new battery materials and designs. Ina genericsense, MD relinquishes the precise quantum calculation (to significantly reduce computational cost) and adopts a much cheaper classical approach based on Newtonian mechanics to cover much larger time and length scales. However, in some cases, it can be closely integrated with quantum calculation to retain some advantages of both worlds. In the following, we sample some examples from battery research to illustrate these points. 7 rari r 1 7 e ee 1 1 1 1 a het Tatnamd ° a o«e 1

Table 2 Molecular dynamics (MD) simulation is an extremely useful tool in battery research as it provides critical information for improving battery performance and developing new battery materials and designs. Ina genericsense, MD relinquishes the precise quantum calculation (to significantly reduce computational cost) and adopts a much cheaper classical approach based on Newtonian mechanics to cover much larger time and length scales. However, in some cases, it can be closely integrated with quantum calculation to retain some advantages of both worlds. In the following, we sample some examples from battery research to illustrate these points. 7 rari r 1 7 e ee 1 1 1 1 a het Tatnamd ° a o«e 1

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paradigm. Hohenberg and Kohn [11,12] are widely regarded inventors of the modern density functional theory (DFT, discovered in 1964). It isa quantum-mechanical atomistic simulation method used to calculate the properties of atomic systems such as atoms, molecules, crystals, and microscopic surfaces. The discovery of new materials with distinct properties and functions with the aid of computational protocols has transformed the entire research community in pure science, materials science, biomedical science, engineering science, and so on. As a sub-set, almost all aspects of battery science and technologies are also transformed, and this is the prime theme of this article. Massive amounts of material information and data have been generated by these computational and experimental tools, resulting in ‘big data’. These big data are exploited by artificial intelligence (Al) and machine learning (ML) to produce meaningful results in material behaviors in science and technology, resulting in the fourth paradigm. Materials 4.0 [13] is another name for this.
paradigm. Hohenberg and Kohn [11,12] are widely regarded inventors of the modern density functional theory (DFT, discovered in 1964). It isa quantum-mechanical atomistic simulation method used to calculate the properties of atomic systems such as atoms, molecules, crystals, and microscopic surfaces. The discovery of new materials with distinct properties and functions with the aid of computational protocols has transformed the entire research community in pure science, materials science, biomedical science, engineering science, and so on. As a sub-set, almost all aspects of battery science and technologies are also transformed, and this is the prime theme of this article. Massive amounts of material information and data have been generated by these computational and experimental tools, resulting in ‘big data’. These big data are exploited by artificial intelligence (Al) and machine learning (ML) to produce meaningful results in material behaviors in science and technology, resulting in the fourth paradigm. Materials 4.0 [13] is another name for this.
Table 1. Summary of computational studies of Liand non-Li advanced batteries by DFT calculations
Table 1. Summary of computational studies of Liand non-Li advanced batteries by DFT calculations
Figure 2. (a) Crystal structures of Li,S, LLZO, LGPS, and LATP, (b) Lit probability density at 900K, (c) Li probability density (cross-section) from the blue plane in (b) [40]. Copyright 2019. Reproduced with permission from John Wiley and Sons Using their calculated conductivities, a phenomenological model was built to account for the effects of GBs on a polycrystalline material. They elucidated that ion conduction occurs through two competing processes in such a material: (i) the granular pathway, which dominates when the GBs have much larger resistance than the bulk crystalline portions, and (ii) the GB pathway, which occurs when conduction in GBs is comparable in magnitude to the bulk electrolyte (e.g., in some sulfides and solid oxides), as shown in Figure 3 (a) and (b). They plotted the total conductivity of Liz3O0Cl against grain size (shown in Figure 3c), which
Figure 2. (a) Crystal structures of Li,S, LLZO, LGPS, and LATP, (b) Lit probability density at 900K, (c) Li probability density (cross-section) from the blue plane in (b) [40]. Copyright 2019. Reproduced with permission from John Wiley and Sons Using their calculated conductivities, a phenomenological model was built to account for the effects of GBs on a polycrystalline material. They elucidated that ion conduction occurs through two competing processes in such a material: (i) the granular pathway, which dominates when the GBs have much larger resistance than the bulk crystalline portions, and (ii) the GB pathway, which occurs when conduction in GBs is comparable in magnitude to the bulk electrolyte (e.g., in some sulfides and solid oxides), as shown in Figure 3 (a) and (b). They plotted the total conductivity of Liz3O0Cl against grain size (shown in Figure 3c), which
Figure 3. (a) Granular conduction (b) GB conduction pathways for Li ions. The orange areas represent the grains, the gray areas are GBs, and the blue particles are Li ions, (c) Total conductivity of LizOCl as grain size changes at 300K, (d), (e) and (f) are Li-ion density maps showing the trajectories of Li-ions near the GBs and bulk LizOCl. Copyright 2018. Reproduced with permission from ACS Publications [42]
Figure 3. (a) Granular conduction (b) GB conduction pathways for Li ions. The orange areas represent the grains, the gray areas are GBs, and the blue particles are Li ions, (c) Total conductivity of LizOCl as grain size changes at 300K, (d), (e) and (f) are Li-ion density maps showing the trajectories of Li-ions near the GBs and bulk LizOCl. Copyright 2018. Reproduced with permission from ACS Publications [42]
Artificial intelligence and machine learning Artificial intelligence (Al) and machine learning (ML) are the most important pillars of the fourth paradigm [55,56] in materials discovery. These tools leverage vast amounts of available data and algorithms to automate decision-making, uncover hidden patterns and relationships, and enable predictions and optimizations at much faster rates than previously possible. By incorporating these advanced technologies, materials scientists and engineers can discover new materials faster, predict their properties more accurately, and design and synthesize more efficient materials for diverse applications. A few examples of how ML can be leveraged are. eae Natarial nrnnarty nradictinn: MI alonrithme ran ha trainad far tha nradictinn anf nrnnartiac
Artificial intelligence and machine learning Artificial intelligence (Al) and machine learning (ML) are the most important pillars of the fourth paradigm [55,56] in materials discovery. These tools leverage vast amounts of available data and algorithms to automate decision-making, uncover hidden patterns and relationships, and enable predictions and optimizations at much faster rates than previously possible. By incorporating these advanced technologies, materials scientists and engineers can discover new materials faster, predict their properties more accurately, and design and synthesize more efficient materials for diverse applications. A few examples of how ML can be leveraged are. eae Natarial nrnnarty nradictinn: MI alonrithme ran ha trainad far tha nradictinn anf nrnnartiac
HE ON ee EE Lae ar Oe eee All ML algorithms intrinsically rely on data. Higher amounts of data lead to, in general, more ccurate models. The data quality is an absolutely important factor since they might lead tc visleading ML predictions (e.g., problems caused by unreproducible data or error-infested data) upervised ML algorithms map the input space to the output space by building a mathematical 1odel that uses data to fine-tune the model parameters during training. One of the most prominent tudies aimed to determine the factors influencing the high conductivity of the Li-ions in the solic xide electrolytes having garnet-like structures (Kireeva et al. [69]). Solid electrolytes increase nergy density by reviving the use of metallic Li anodes and alleviate much concern of liquic lectrolytes typically used in commercial LIBs: toxicity, flammability, and limited electrochemical findow. Their entire dataset, when combined with descriptors that encode information about the vaterials’ production, had 168 compounds. To assess the Li-ion transport properties of the data anc stablish the composition-structure-ionic conductivity correlations, support vector machines (SVM. ere employed in a regression analysis. Following that, the model was utilized to scan garnet alated structures for viable compositions and offer a bird’s eye perspective of the solid electrolyte Jaterials space, which is appealing for virtual screening, as shown in Figure 5. _ =
HE ON ee EE Lae ar Oe eee All ML algorithms intrinsically rely on data. Higher amounts of data lead to, in general, more ccurate models. The data quality is an absolutely important factor since they might lead tc visleading ML predictions (e.g., problems caused by unreproducible data or error-infested data) upervised ML algorithms map the input space to the output space by building a mathematical 1odel that uses data to fine-tune the model parameters during training. One of the most prominent tudies aimed to determine the factors influencing the high conductivity of the Li-ions in the solic xide electrolytes having garnet-like structures (Kireeva et al. [69]). Solid electrolytes increase nergy density by reviving the use of metallic Li anodes and alleviate much concern of liquic lectrolytes typically used in commercial LIBs: toxicity, flammability, and limited electrochemical findow. Their entire dataset, when combined with descriptors that encode information about the vaterials’ production, had 168 compounds. To assess the Li-ion transport properties of the data anc stablish the composition-structure-ionic conductivity correlations, support vector machines (SVM. ere employed in a regression analysis. Following that, the model was utilized to scan garnet alated structures for viable compositions and offer a bird’s eye perspective of the solid electrolyte Jaterials space, which is appealing for virtual screening, as shown in Figure 5. _ =
Figure 6. Magnified views of Li diffusion atthe interfacial region of the (a) a-Li/Si<110> and (b) a-Li/Si<111> diffusion couples [100]. Copyright 2017. Reproduced with permission from Springer Nature use of morphologically modified nanostructures such as porous Si [92-94], Si nanowires [95], core- shell structures [96,97], hollow nanoparticles [98,99] are reported to date. However, obtaining ultrafast chargeable and long-term stable Si anode material is still challenging. The main challenge arises during the intercalation process [89] when the silicon anode experiences a large volume expansion 300 % owing to a large intake of Li per Si atom. Such a huge volume expansion creates enormous stress on its structure and leads to mechanical failure/fracture. This further causes electrolyte decomposition by providing direct contact with the Si anode and electrolyte. Chan etal. [101] and Jung etal. [102] showed that the process of lithiation and delithiation of crystalline Si is anisotropic and favors (110) surface over (100) and (111) surfaces due to its small interfacial energy at the interface of amorphous Li,Si/crystalline Si. This promotes lithiation behind the interface and causes volume expansion along <110>. The amorphization process of Si and diffusion of Li ions during cycling plays an important role in the performance of the Si anode. Pan et al. calculated the average coordination number (CN) for the interaction of Li ions in amorphous Si and suggested that stress canincrease Liion diffusion either by increasing free volume under tension or by changing local structure during compression [103]. Choi et a/. [100] found that Li-ion diffusion in c-Si follows tortuous diffusion pathways and the degree of the tortuosity varies with the orientation of Si. Thus, Li-ion diffusion through Si<110> tends to take a less winding path to movea particular distance when compared with diffusion through Si<111>. The Li ions diffusion pathway that migrates through the different lattices is shown in Figure 6.
Figure 6. Magnified views of Li diffusion atthe interfacial region of the (a) a-Li/Si<110> and (b) a-Li/Si<111> diffusion couples [100]. Copyright 2017. Reproduced with permission from Springer Nature use of morphologically modified nanostructures such as porous Si [92-94], Si nanowires [95], core- shell structures [96,97], hollow nanoparticles [98,99] are reported to date. However, obtaining ultrafast chargeable and long-term stable Si anode material is still challenging. The main challenge arises during the intercalation process [89] when the silicon anode experiences a large volume expansion 300 % owing to a large intake of Li per Si atom. Such a huge volume expansion creates enormous stress on its structure and leads to mechanical failure/fracture. This further causes electrolyte decomposition by providing direct contact with the Si anode and electrolyte. Chan etal. [101] and Jung etal. [102] showed that the process of lithiation and delithiation of crystalline Si is anisotropic and favors (110) surface over (100) and (111) surfaces due to its small interfacial energy at the interface of amorphous Li,Si/crystalline Si. This promotes lithiation behind the interface and causes volume expansion along <110>. The amorphization process of Si and diffusion of Li ions during cycling plays an important role in the performance of the Si anode. Pan et al. calculated the average coordination number (CN) for the interaction of Li ions in amorphous Si and suggested that stress canincrease Liion diffusion either by increasing free volume under tension or by changing local structure during compression [103]. Choi et a/. [100] found that Li-ion diffusion in c-Si follows tortuous diffusion pathways and the degree of the tortuosity varies with the orientation of Si. Thus, Li-ion diffusion through Si<110> tends to take a less winding path to movea particular distance when compared with diffusion through Si<111>. The Li ions diffusion pathway that migrates through the different lattices is shown in Figure 6.
mete nseomienige «wae, Reet a, AF Jiang et al. [105] carried out quantitative analysis by proposing a zero-dimensional mechanisti model of Si anodes for LIBs and showed that a slower crystallization rate leads to the abrup exponential growth of crystalline silicon, while increased crystallization rate gives a sigmoida profile. Figure 7 (a) shows the different voltage curves. Figure 7 (b) depicts that the sufficien increase in the surface energy barrier (E*) causes the elimination of higher voltage peaks from the differential analysis of Si anode and, therefore, other techniques will be necessary to analyze Li-S chemistry. In addition, Figure (7 (c) and (d)) indicates the variation in the growth rate of crystalline and amorphous phases of Li-Si composition with the increment of surface barrier energy. These results confirmed the important role of higher surface barrier energy behind the poor performance of bulk Si anode compared to Si nanoparticles.
mete nseomienige «wae, Reet a, AF Jiang et al. [105] carried out quantitative analysis by proposing a zero-dimensional mechanisti model of Si anodes for LIBs and showed that a slower crystallization rate leads to the abrup exponential growth of crystalline silicon, while increased crystallization rate gives a sigmoida profile. Figure 7 (a) shows the different voltage curves. Figure 7 (b) depicts that the sufficien increase in the surface energy barrier (E*) causes the elimination of higher voltage peaks from the differential analysis of Si anode and, therefore, other techniques will be necessary to analyze Li-S chemistry. In addition, Figure (7 (c) and (d)) indicates the variation in the growth rate of crystalline and amorphous phases of Li-Si composition with the increment of surface barrier energy. These results confirmed the important role of higher surface barrier energy behind the poor performance of bulk Si anode compared to Si nanoparticles.
Additionally, their results confirmed that the phosphides usually display a comparatively inferior reaction potential compared to oxides and sulfides. There are eighteen binary compounds that showed low reaction potential and high theoretical capacity (0.7 V, greater than 450 mAh gt, respectively) that were thought of as promising KIBs anode materials. In specific, V3Sa, CuP2, ZnP2, NiP3, SiP, Nb3Ss, CoP3, Ga2O3 BizO3, showed high theoretical capacities. The reaction potential and theoretical capacities are shown in Figure 9. The alloy-type compounds P, Sb, Bi As, Si, and Ge showed higher capacities than the conventional graphite (C) anodes. Screening helped to identify eighteen binary compounds (four oxides, six sulfides, and eight phosphides) with high capacity and lower reaction potential. Amongst these phosphides, higher capacities were observed for SiP CuP2 and ZnP2 (681, 712 and 842 mAh g*) with average potentials of 0.60, 0.50 and 0.51 V, respectively. In the case of sulfides, Nb3Ss and V3S4 exhibited capacities of 611 and 763 mAh g* with average potentials of 0.69 and 0.61 V, correspondingly. Among the oxides, GazO3 and Biz03 showed high theoretical capacities of 690 and 524 mAh g*, respectively. Usually, the alloy-type material with high theoretical capacities showed unusually high-volume expansion to the tune of 200 to 600 %, which could result in capacity degradation during continuous charging -discharging cycles. Consequently, volume expansion control
Additionally, their results confirmed that the phosphides usually display a comparatively inferior reaction potential compared to oxides and sulfides. There are eighteen binary compounds that showed low reaction potential and high theoretical capacity (0.7 V, greater than 450 mAh gt, respectively) that were thought of as promising KIBs anode materials. In specific, V3Sa, CuP2, ZnP2, NiP3, SiP, Nb3Ss, CoP3, Ga2O3 BizO3, showed high theoretical capacities. The reaction potential and theoretical capacities are shown in Figure 9. The alloy-type compounds P, Sb, Bi As, Si, and Ge showed higher capacities than the conventional graphite (C) anodes. Screening helped to identify eighteen binary compounds (four oxides, six sulfides, and eight phosphides) with high capacity and lower reaction potential. Amongst these phosphides, higher capacities were observed for SiP CuP2 and ZnP2 (681, 712 and 842 mAh g*) with average potentials of 0.60, 0.50 and 0.51 V, respectively. In the case of sulfides, Nb3Ss and V3S4 exhibited capacities of 611 and 763 mAh g* with average potentials of 0.69 and 0.61 V, correspondingly. Among the oxides, GazO3 and Biz03 showed high theoretical capacities of 690 and 524 mAh g*, respectively. Usually, the alloy-type material with high theoretical capacities showed unusually high-volume expansion to the tune of 200 to 600 %, which could result in capacity degradation during continuous charging -discharging cycles. Consequently, volume expansion control
Figure 13. Theoretical specific energies of various types of metal-air batteries [163]. Copyright 2017. Reproduced with permission from ACS publication where M is a metal and n is the oxidation number of that metal. While charging, a reverse reaction occurs where metals are plated on the anode and gaseous oxygen is evolved at the cathode. A comparison of the theoretical energy densities of some of the prominent metal-air battery systems is shown in Figure 13.
Figure 13. Theoretical specific energies of various types of metal-air batteries [163]. Copyright 2017. Reproduced with permission from ACS publication where M is a metal and n is the oxidation number of that metal. While charging, a reverse reaction occurs where metals are plated on the anode and gaseous oxygen is evolved at the cathode. A comparison of the theoretical energy densities of some of the prominent metal-air battery systems is shown in Figure 13.
Table 3. List of different battery properties studied from a computational approach. Multiple avenues for increasing the safety and performance of LIBs are extensively explored anc many of them are likely to be adopted by many industries in the near future. As a competitive substitute for LIB batteries beyond Li, in general, multivalent types, in particular, are considerec potential candidates for energy storage systems that can be deployed at large scales. In that regard, strategic computational materials and cell development are anticipated to play significant roles ir the analysis and investigation of battery properties in all length scales (atom to device), as shown ir Figure 14 and Table 3.
Table 3. List of different battery properties studied from a computational approach. Multiple avenues for increasing the safety and performance of LIBs are extensively explored anc many of them are likely to be adopted by many industries in the near future. As a competitive substitute for LIB batteries beyond Li, in general, multivalent types, in particular, are considerec potential candidates for energy storage systems that can be deployed at large scales. In that regard, strategic computational materials and cell development are anticipated to play significant roles ir the analysis and investigation of battery properties in all length scales (atom to device), as shown ir Figure 14 and Table 3.
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