MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes

Kim, Sungjin; Jinich, Adrián; Aspuru‐Guzik, Alán

doi:10.1021/acs.jcim.6b00332

Cited by 28 publications

(20 citation statements)

References 86 publications

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“…The versatility of organic chemistry and the need for higher capacity (low molecular weight) opens a large podium of different configurations and possibilities, and their performance should be carefully checked. That is recently more actively performed with computational modeling, which can predict properties of novel redox couples with enhanced performance or stability, [203] and with the development of machine learning [204] and advanced robotics. [205] There are two types of redox-active organic materials, n-type where cations coordinate the redox centers, and p-type, where the redox centers are coordinated by anions.…”

Section: Organic Battery Materialsmentioning

confidence: 99%

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries.Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time-and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metalsulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.

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Section: Organic Battery Materialsmentioning

confidence: 99%

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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“…To this end, many predictive models based on experimental data sets or molecular descriptors have been developed 82,83 and were employed in some RFB studies 80,84 . A very promising method for predicting aqueous solubility of RFB candidates in high‐throughput screening approaches was developed by Aspuru‐Guzik and co‐workers 85 . Tabor et al addressed the problem of durability of quinones in water with a thorough investigation of the relationship between reduction potential and thermodynamic stability 53 .…”

Section: Organic Redox Flow Battery Materialsmentioning

confidence: 99%

Molecular modeling of organic redox‐active battery materials

Fornari

Silva

2020

WIREs Comput Mol Sci

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Organic redox‐active battery materials are an emerging alternative to their inorganic counterparts currently used in the commercialized battery technologies. The main advantages of organic batteries are the potential for low‐cost manufacturing, tunability of electrochemical properties through molecular engineering, and their environmental sustainability. The search for organic electroactive materials that could be used for energy storage in mobile and stationary applications is an active area of research. Computer simulations are used extensively to improve the understanding of the fundamental processes in the existing materials and to accelerate the discovery of new materials with improved performance. We will focus on two main types of redox‐active organic battery materials, that is, solid‐state organic electrode materials and organic electrolytes for redox flow batteries. Because organic materials are made of molecular building blocks, the molecular modeling methodology is usually the most appropriate to investigate their properties at the electronic and atomistic scales. After introducing the fundamentals of computational organic electrochemistry, we will survey its most recent applications in organic battery research and outline some of the remaining challenges for the development and applications of atomic‐scale modeling techniques in the organic battery context. This article is categorized under: Structure and Mechanism > Computational Materials Science Software > Molecular Modeling Electronic Structure Theory > Density Functional Theory

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“…Kim et al. reported the usage of free energy of solvation as a proxy descriptor to predict the solubility of organic electrolytes of flow battery electrolyte solutions . The vast field of organic electronics technology also greatly benefits from an improved accuracy in describing solubility, from organic light emitting diodes (OLEDs), organic transistors, and organic solar cells .…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Predict Solubility Parameters

Sánchez-Lengeling

Roch

Perea

et al. 2018

Advcd Theory and Sims

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Solubility is a ubiquitous phenomenon in many aspects of material science. While solubility can be determined by considering the cohesive forces in a liquid via the Hansen solubility parameters (HSP), quantitative structure-property relationship models are often used for prediction, notably due to their low computational cost. Here, gpHSP, an interpretable and versatile probabilistic approach to determining HSP, is reported. Our model is based on Gaussian processes, a Bayesian machine learning approach that provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP, including a general solvents set aggregated from the literature, and a polymer set experimentally characterized by this group of authors. In all sets, a high degree of agreement is obtained, surpassing well-established machine learning methods. The general applicability of gpHSP to miscibility of organic semiconductors, drug compounds, and in general solvents is demonstrated, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.

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MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes

Cited by 28 publications

References 86 publications

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Molecular modeling of organic redox‐active battery materials

A Bayesian Approach to Predict Solubility Parameters

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