scholarly journals Predictive stochastic analysis of massive filter-based electrochemical reaction networks

Author(s):  
Daniel Barter ◽  
Evan Walter Clark Spotte-Smith ◽  
Nikita S. Redkar ◽  
Shyam Dwaraknath ◽  
Kristin A. Persson ◽  
...  

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.

2021 ◽  
Author(s):  
Daniel Barter ◽  
Evan Walter Clark Spotte-Smith ◽  
Nikita S. Redkar ◽  
Shyam Dwaraknath ◽  
Kristin A. Persson ◽  
...  

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering multiple novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.


2020 ◽  
Author(s):  
Nataliia Mozhzhukhina ◽  
Eibar Flores ◽  
Robin Lundström ◽  
Ville Nystrom ◽  
Paul Kitz ◽  
...  

<div>The solid electrolyte interphase (SEI) is one of the most critical, yet least understood, components to guarantee a </div><div>stable, long-lived and safe operation of the Li-ion cell. Herein, the early stages of SEI formation in a typical commercially-available </div><div>LiPF<sub>6</sub> and organic carbonate based Li-ion electrolyte are explored by <i>operando</i> surface enhanced Raman spectroscopy (SERS), </div><div>online electrochemical mass spectrometry (OEMS), and electrochemical quartz crystal microbalance (EQCM). The electric double-</div><div>layer is directly observed to charge as Li<sup>+</sup> solvated by EC progressively accumulates at the negatively charged electrode surface. </div><div>Further negative polarization triggers SEI formation as evidenced by H<sub>2</sub> evolution, electrode mass deposition, and expulsion of the </div><div>electrolyte from the electrode surface. Electrolyte impurities, such as HF and H<sub>2</sub>O, are reduced early and contribute in a multistep </div><div>electro-/chemical process to an inorganic SEI layer rich in LiF and Li<sub>2</sub>CO<sub>3</sub>. These results underline the strong influence of trace </div><div>impurities on the buildup of the SEI layer, and give new insight into the formation mechanism of the multi-layered SEI. The presented </div><div>study is a model example of how a combination of complementary and highly surface-sensitive operando characterization techniques </div><div>offer a step forward to understand interfacial phenomenon and SEI formation mechanisms in future Li-ion batteries</div>


2020 ◽  
Author(s):  
Nataliia Mozhzhukhina ◽  
Eibar Flores ◽  
Robin Lundström ◽  
Ville Nystrom ◽  
Paul Kitz ◽  
...  

<div>The solid electrolyte interphase (SEI) is one of the most critical, yet least understood, components to guarantee a </div><div>stable, long-lived and safe operation of the Li-ion cell. Herein, the early stages of SEI formation in a typical commercially-available </div><div>LiPF<sub>6</sub> and organic carbonate based Li-ion electrolyte are explored by <i>operando</i> surface enhanced Raman spectroscopy (SERS), </div><div>online electrochemical mass spectrometry (OEMS), and electrochemical quartz crystal microbalance (EQCM). The electric double-</div><div>layer is directly observed to charge as Li<sup>+</sup> solvated by EC progressively accumulates at the negatively charged electrode surface. </div><div>Further negative polarization triggers SEI formation as evidenced by H<sub>2</sub> evolution, electrode mass deposition, and expulsion of the </div><div>electrolyte from the electrode surface. Electrolyte impurities, such as HF and H<sub>2</sub>O, are reduced early and contribute in a multistep </div><div>electro-/chemical process to an inorganic SEI layer rich in LiF and Li<sub>2</sub>CO<sub>3</sub>. These results underline the strong influence of trace </div><div>impurities on the buildup of the SEI layer, and give new insight into the formation mechanism of the multi-layered SEI. The presented </div><div>study is a model example of how a combination of complementary and highly surface-sensitive operando characterization techniques </div><div>offer a step forward to understand interfacial phenomenon and SEI formation mechanisms in future Li-ion batteries</div>


2020 ◽  
Author(s):  
Samuel Blau ◽  
Hetal Patel ◽  
Evan Spotte-Smith ◽  
Xiaowei Xie ◽  
Shyam Dwaraknath ◽  
...  

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B reacts to C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6,000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.


2020 ◽  
Author(s):  
Samuel Blau ◽  
Hetal Patel ◽  
Evan Spotte-Smith ◽  
Xiaowei Xie ◽  
Shyam Dwaraknath ◽  
...  

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B reacts to C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6,000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.


2021 ◽  
Author(s):  
Xiaowei Xie ◽  
Evan Spotte-Smith ◽  
Hetal Patel ◽  
Samuel Blau ◽  
Kristin Persson

<div><div><div><p>Interfacial reactions are notoriously difficult to characterize and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid–electrolyte interphase (SEI), critical to the operation of Li-ion batteries (LIB), exemplifies such a surface film and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. In this work, we present pioneering results of a newly developed data-driven reaction network addressing the recent question whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for our vast thermodynamic reaction landscape, calculated with density functional theory (DFT). The graph implementation of the reaction landscape is subsequently explored using shortest pathfinding algorithms, identifying reactions to LEMC from EC, Li+ and H2O under the electron chemical potential of Li metal. Confirming the viability of our approach, the reaction network automatically recovers previously-proposed formation mechanisms of LEMC from EC and LEDC through hydrolysis, among which the direct hydrolysis of EC under basic conditions is found to be the most kinetically favorable. We also identify several other new reaction pathways to LEMC, illustrating the complex and competitive landscape of possible electrochemical electrolyte decomposition reactions. For example, we recover a LEMC formation mechanism that generates lithium hydride as a by-product and a radical mechanism through breaking the (CH2)O–C(–O)OLi bond in LEDC, neither of which has been proposed previously. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared to LEDC, a conclusion that has direct impact on our understanding of SEI formation in Li-ion energy storage systems. Finally, we emphasize that our framework demonstrates robust, automated, data-driven predictions of novel interfacial reaction mechanisms and this framework is generally applicable to other reactive systems.</p></div></div></div>


2021 ◽  
Author(s):  
Xiaowei Xie ◽  
Evan Spotte-Smith ◽  
Hetal Patel ◽  
Samuel Blau ◽  
Kristin Persson

<div><div><div><p>Interfacial reactions are notoriously difficult to characterize and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid–electrolyte interphase (SEI), critical to the operation of Li-ion batteries (LIB), exemplifies such a surface film and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. In this work, we present pioneering results of a newly developed data-driven reaction network addressing the recent question whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for our vast thermodynamic reaction landscape, calculated with density functional theory (DFT). The graph implementation of the reaction landscape is subsequently explored using shortest pathfinding algorithms, identifying reactions to LEMC from EC, Li+ and H2O under the electron chemical potential of Li metal. Confirming the viability of our approach, the reaction network automatically recovers previously-proposed formation mechanisms of LEMC from EC and LEDC through hydrolysis, among which the direct hydrolysis of EC under basic conditions is found to be the most kinetically favorable. We also identify several other new reaction pathways to LEMC, illustrating the complex and competitive landscape of possible electrochemical electrolyte decomposition reactions. For example, we recover a LEMC formation mechanism that generates lithium hydride as a by-product and a radical mechanism through breaking the (CH2)O–C(–O)OLi bond in LEDC, neither of which has been proposed previously. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared to LEDC, a conclusion that has direct impact on our understanding of SEI formation in Li-ion energy storage systems. Finally, we emphasize that our framework demonstrates robust, automated, data-driven predictions of novel interfacial reaction mechanisms and this framework is generally applicable to other reactive systems.</p></div></div></div>


2020 ◽  
Author(s):  
Paul Kitz ◽  
Matthew Lacey ◽  
Petr Novák ◽  
Erik Berg

<div>The electrolyte additives vinylene carbonate (VC) and fluoroethylene carbonate (FEC) are well known for increasing the lifetime of a Li-ion battery cell by supporting the formation of an effective solid electrolyte interphase (SEI) at the anode. In this study combined simultaneous electrochemical impedance spectroscopy (EIS) and <i>operando</i> electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D) are employed together with <i>in situ</i> gas analysis (OEMS) to study the influence of VC and FEC on the passivation process and the interphase properties at carbon-based anodes. In small quantities both additives reduce the initial interphase mass loading by 30 to 50 %, but only VC also effectively prevents continuous side reactions and improves anode passivation significantly. VC and FEC are both reduced at potentials above 1 V vs. Li<sup>+</sup>/Li in the first cycle and change the SEI composition which causes an increase of the SEI shear storage modulus by over one order of magnitude in both cases. As a consequence, the ion diffusion coefficient and conductivity in the interphase is also significantly affected. While small quantities of VC in the initial electrolyte increase the SEI conductivity, FEC decomposition products hinder charge transport through the SEI and thus increase overall anode impedance significantly. </div>


2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>


2019 ◽  
Vol 26 (8) ◽  
pp. 1311-1327 ◽  
Author(s):  
Pala Rajasekharreddy ◽  
Chao Huang ◽  
Siddhardha Busi ◽  
Jobina Rajkumari ◽  
Ming-Hong Tai ◽  
...  

With the emergence of nanotechnology, new methods have been developed for engineering various nanoparticles for biomedical applications. Nanotheranostics is a burgeoning research field with tremendous prospects for the improvement of diagnosis and treatment of various cancers. However, the development of biocompatible and efficient drug/gene delivery theranostic systems still remains a challenge. Green synthetic approach of nanoparticles with low capital and operating expenses, reduced environmental pollution and better biocompatibility and stability is a latest and novel field, which is advantageous over chemical or physical nanoparticle synthesis methods. In this article, we summarize the recent research progresses related to green synthesized nanoparticles for cancer theranostic applications, and we also conclude with a look at the current challenges and insight into the future directions based on recent developments in these areas.


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