A neural network based approach to predict high voltage li-ion battery cathode materials

Sarkar, T.; Sharma, Alind; Das, A. K.; Deodhare, Dipti; Bharadwaj, Mridula Dixit

doi:10.1109/icdcsyst.2014.6926140

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Instead of training ML potentials to approximate the PES, ML prediction models can be trained to target specific properties (Property Predictions). Sarkar et al firstly trained an ANN using electronegativity as the descriptor to predict the voltages of several cathode materials (Sarkar et al, 2014). Although the ANN model did not reach first-principles accuracy due to the limited size of the dataset, it still paved the way to screen Licontaining compounds for cathode materials.…”

Section: Cathodesmentioning

confidence: 99%

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

et al. 2021

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Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

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Section: Cathodesmentioning

confidence: 99%

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

et al. 2021

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“…62,63 In each case, multiple structural characteristics, such as the numbers of oxygen atoms, boron atoms, carbon atoms, and aromatic rings, are used to predict the voltage, capacity, or charge. 64,65 However, in order to use ML as a planning tool, guiding what data to include, what simulations to schedule, or what experiments to try, screening must be done in advance of research based on the chemical composition alone. Prescreening before costly or toxic materials are made, energyconsuming simulations are run, or time-consuming characterization is undertaken would only be possible if structural information can be safely omitted.…”

Section: ■ Introductionmentioning

confidence: 99%

“…However, modern technologies such as electric cars and wireless electronic devices demand new high-capacity battery materials , that reduce our dependence on Li, which is an expensive and volatile commodity . This has motivated research into alternative materials, such as sodium-ion batteries, , to make the energy economy more sustainable. , ML has been used to predict the electrochemical potential for new cathode materials and establish quantitative molecular structure-redox potential relationships to help find new and stable battery materials. , In each case, multiple structural characteristics, such as the numbers of oxygen atoms, boron atoms, carbon atoms, and aromatic rings, are used to predict the voltage, capacity, or charge. , However, in order to use ML as a planning tool, guiding what data to include, what simulations to schedule, or what experiments to try, screening must be done in advance of research based on the chemical composition alone. Prescreening before costly or toxic materials are made, energy-consuming simulations are run, or time-consuming characterization is undertaken would only be possible if structural information can be safely omitted.…”

Section: Introductionmentioning

confidence: 99%

Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning

Zhuang,

Barnard

2023

Chem. Mater.

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Machine learning is a powerful tool to predict the properties of materials for a variety of applications. However, generating data sets of carefully characterized materials can be time-consuming and costly, particularly when numerous candidate materials are later found to be irrelevant. The problem could be alleviated if machine learning can be used with minimal information to provide guidance at an early stage before significant investment has been made. Since structural characterization is one of the most expensive parts of the process, this study explores structure-free encoding of materials using Mendeleev encoding, a method that does not require information such as lattice constants, lattice positions, or bonding networks. We evaluate Mendeleev encoding using three data sets of continuous, complex material compounds used for battery applications, with four different unsupervised learning methods, inclusive of six algorithms and four evaluation metrics and in addition visualizations of the results. Our results show that Mendeleev encoding is more accurate, stable, and reliable than alternative structure-free encoding, allowing both principle component analysis and archetypal analysis to capture more of the variance during dimensionality reduction and consistently provide superior clustering results. Mendeleev encoding is a simple and scientifically intuitive way of representing material data that is both human and machine-readable and is applicable to any machine-learning task training with tabular data.

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“…28,29 In each case multiple structural characteristics, such as the numbers of oxygen atoms, lithium atoms, boron atoms, carbon atoms, and aromatic rings, are used to individually predict the voltage, capacity, or charge. 30,31 Recently a general ML approach was developed based on multi-target random forests that accurately predicts inverse property/structure relationships. The method is capable of outputting a set of target physicochemical characteristics that can simultaneously deliver a predefined set of properties without the need for additional optimization or an exhaustive data set that makes a priori prediction redundant.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Provided sufficient data are available, machine learning (ML) provides a convenient way of rapidly predicting the electrochemical properties of MXenes (the target labels) based on the physicochemical characteristics (the structural features). , These are termed structure/property relationships and are capable of providing accurate predictions for a variety of candidate materials for energy applications. − Machine learning models have been used to show that the electrochemical potential can be predicted for new cathode materials, and the quantitative molecular structure–redox potentials relationships can be established. , In each case multiple structural characteristics, such as the numbers of oxygen atoms, lithium atoms, boron atoms, carbon atoms, and aromatic rings, are used to individually predict the voltage, capacity, or charge. , Recently a general ML approach was developed based on multi-target random forests that accurately predicts inverse property/structure relationships. The method is capable of outputting a set of target physicochemical characteristics that can simultaneously deliver a predefined set of properties without the need for additional optimization or an exhaustive data set that makes a priori prediction redundant …”

Section: Introductionmentioning

confidence: 99%

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning

Barnard

2022

Chem. Mater.

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There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li 2 M 2 C and Mg 2 M 2 C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge.

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A neural network based approach to predict high voltage li-ion battery cathode materials

Cited by 8 publications

References 16 publications

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning

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