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.

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Machine learning in materials informatics: recent applications and prospects

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.

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Accelerating the discovery of materials for clean energy in the era of smart automation

This Review describes the state-of-the-art of wearable electronics (smart textiles). The unique and promising advantages of smart electronic textiles are highlighted by comparing them with the conventional planar counterparts. The main kinds of smart electronic textiles based on different functionalities, namely the generation, storage, and utilization of electricity, are then discussed with an emphasis on the use of functional materials. The remaining challenges are summarized together with important new directions to provide some useful clues for the future development of smart electronic textiles.

Smart Electronic Textiles.

Conducting polymers (CPs), thanks to their unique properties, structures made on-demand, new composite mixtures, and possibility of deposit on a surface by chemical, physical, or electrochemical methodologies, have shown in the last years a renaissance and have been widely used in important fields of chemistry and materials science. Due to the extent of the literature on CPs, this review, after a concise introduction about the interrelationship between electrochemistry and conducting polymers, is focused exclusively on the following applications: energy (energy storage devices and solar cells), use in environmental remediation (anion and cation trapping, electrocatalytic reduction/oxidation of pollutants on CP based electrodes, and adsorption of pollutants) and finally electroanalysis as chemical sensors in solution, gas phase, and chiral molecules. This review is expected to be comprehensive, authoritative, and useful to the chemical community interested in CPs and their applications.

Conducting Polymers in the Fields of Energy, Environmental Remediation, and Chemical–Chiral Sensors

Electrochemical nanoarchitectonics and layer-by-layer assembly: From basics to future

Electrochromic fabric devices represent a further extension to the plethora of available literature on conductive fabrics. This article contains a brief overview of electrochromic devices, electrochromic polymers, conductive materials, conductive fabrics, and electrochromic fabric devices. A tabulated list of the perceived colour of a number of electrochromic polymers that is contained herein is designed to serve as an aide to colour mixing studies. The challenges of optimisation and commercialisatiion of electrochromic fabric devices and their mitigating factors are conjectured, along with some future potential applications for electrochromic fabric device technologies.

A review of organic electrochromic fabric devices

The rational design of polyurea & polyurethane dielectric materials

A series of smectic liquid crystalline network polymer was subjected to a large uniaxial stress at temperatures far below the clearing temperature. Their dimensional recovery is anelastic showing substantial retained strain. This process produced a temporally stable monodomain state. Upon heating this monodomain from room temperature, recovery of the original film dimensions occurs. The strain recovery (length) curves show a pronounced curvature as the temperature approaches the smectic-isotropic temperature. It is proposed that nanosegregation of netpoints in the smectic structure is responsible for the anelasticity and that the temperature dependence of the shape recovery is consistent with a balance between enthalpic and entropic forces with temperature. An interesting mechanically induced strains ratio (MISR) was observed on heating these prestressed films. Loss of the monodomain structure near the isotropization temperature is postulated to rationalize the shape of the MISR curves.

Thermal strain recovery of anelastic monodomain liquid crystalline networks: Mechanically induced strains ratios

We study the effect on the microwave S-parameters of adding a conjugated polymer layer to a deoxyribonucleic acid-based (DNA-based) biopolymer within a variable capacitor (varactor) using a capacitive test structure The conjugated polymer we are using in this work is poly(3-hexylthiophene-2,5-diyl) (P3HT) and the DNA-based biopolymer is DNA-CTMA The results are compared to both the baseline DNA-based biopolymer and the conjugated polymer layer added to a layer of polyvinyl alcohol The addition of the conjugated polymer layer underneath the DNA-based biopolymer is found to most significantly improve the stability and dielectric tuning of the DNA-based biopolymer

Whitney M. Kline

Papers

A review of organic electrochromic fabric devices

The rational design of polyurea & polyurethane dielectric materials

Thermal strain recovery of anelastic monodomain liquid crystalline networks: Mechanically induced strains ratios

Response of varactors containing DNA-conjugated polymer