Impact of oxygen-containing carbonyl and ether groups on dielectric properties of poly(oxa)norbornene cyclic olefins

Flexible dielectric polymers that can withstand high electric field and simultaneously have high dielectric constant are desired for high-density energy storage.Here,we systematically investigated the impact of oxygen-containing ether and carbonyl groups in the backbone structure on dielectric properties of a series of cyclic olefin.In comparison to the influence of the-CF3 pendant groups that had more impact on the dielectric constant rather than the band gap,the change of the backbone structure affected both the dielectric constant and band gaps.The one polymer with ether and carbonyl groups in the backbone has the largest band gap and highest discharge efficiency,while it has the lowest dielectric constant.The polymer without any ether groups in the backbone has the smallest band gap and lowest discharge efficiency,but it has the highest dielectric constant.Polymers that have no dipolar relaxation exhibit an inversely correlated dielectric constant and band gap.Enhancing the dipolar relaxation through rational molecular structure design can be a novel way to break through the exclusive constraint of dielectric constant and band gap for high-density energy storage.

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature.We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature.As a component of our pipeline,we trained MaterialsBERT,a language model,using 2.4 million materials science abstracts,which outperforms other baseline models in three out of five named entity recognition datasets.Using this pipeline,we obtained~300,000 material property records from~130,000 abstracts in 60 hours.The extracted data was analyzed for a diverse range of applications such as fuel cells,supercapacitors,and polymer solar cells to recover non-trivial insights.The data extracted through our pipeline is made available at polymerscholar.org which can be used to locate material property data recorded in abstracts.This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with extracted material property information.

A deep learning framework to emulate density functional theory

Density functional theory(DFT)has been a critical component of computational materials research and discovery for decades.However,the computational cost of solving the central Kohn–Sham equation remains a major obstacle for dynamical studies of complex phenomena at-scale.Here,we propose an end-to-end machine learning(ML)model that emulates the essence of DFT by mapping the atomic structure of the system to its electronic charge density,followed by the prediction of other properties such as density of states,potential energy,atomic forces,and stress tensor,by using the atomic structure and charge density as input.Our deep learning model successfully bypasses the explicit solution of the Kohn-Sham equation with orders of magnitude speedup(linear scaling with system size with a small prefactor),while maintaining chemical accuracy.We demonstrate the capability of this ML-DFT concept for an extensive database of organic molecules,polymer chains,and polymer crystals.

Machine learning in materials informatics:recent applications and prospects

Propelled partly by the Materials Genome Initiative,and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains,informatics strategies are beginning to take shape within materials science.These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved.Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost,time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases.Predictions are typically interpolative,involving fingerprinting a material numerically first,and then following a mapping(established via a learning algorithm)between the fingerprint and the property of interest.Fingerprints,also referred to as“descriptors”,may be of many types and scales,as dictated by the application domain and needs.Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account.This article attempts to provide an overview of some of the recent successful data-driven“materials informatics”strategies undertaken in the last decade,with particular emphasis on the fingerprint or descriptor choices.The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Solving the electronic structure problem with machine learning

Simulations based on solving the Kohn-Sham(KS)equation of density functional theory(DFT)have become a vital component of modern materials and chemical sciences research and development portfolios.Despite its versatility,routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation.Here we introduce a machine-learning-based scheme to efficiently assimilate the function of the KS equation,and by-pass it to directly,rapidly,and accurately predict the electronic structure of a material or a molecule,given just its atomic configuration.A new rotationally invariant representation is utilized to map the atomic environment around a grid-point to the electron density and local density of states at that grid-point.This mapping is learned using a neural network trained on previously generated reference DFT results at millions of grid-points.The proposed paradigm allows for the high-fidelity emulation of KS DFT,but orders of magnitude faster than the direct solution.Moreover,the machine learning prediction scheme is strictly linear-scaling with system size.

A universal strategy for the creation of machine learning-based atomistic force fields

Emerging machine learning(ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems.In this contribution,we outline a universal strategy to create ML-based atomistic force fields,which can be used to perform high-fidelity molecular dynamics simulations.This scheme involves(1)preparing a big reference dataset of atomic environments and forces with sufficiently low noise,e.g.,using density functional theory or higher-level methods,(2)utilizing a generalizable class of structural fingerprints for representing atomic environments,(3)optimally selecting diverse and nonredundant training datasets from the reference data,and(4)proposing various learning approaches to predict atomic forces directly(and rapidly)from atomic configurations.From the atomistic forces,accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory.Based on this strategy,we have created model ML force fields for six elemental bulk solids,including Al,Cu,Ti,W,Si,and C,and show that all of them can reach chemical accuracy.The proposed procedure is general and universal,in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention.Moreover,the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

Frequency-dependent dielectric constant prediction of polymers using machine learning

The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature applications.To aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of magnitude.Our model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.