Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.

Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world's plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. In this work, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world's yearly plastic production. We discuss possible synthesis routes for these identified promising materials. The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome.org.

An artificial intelligence technique--machine learning--is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing. Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability. Already, researchers are expanding the model to predict other important MOF properties. Supported by the Office of Science's Basic Energy Sciences program within the U.S. Department of Energy (DOE), the research was reported Nov. 9 in the journal Nature Machine Intelligence. The research was conducted in the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), a DOE Energy Frontier Research Center located at the Georgia Institute of Technology.

Metal-organic frameworks (MOFs) are a class of porous and crystalline materials that are synthesized from inorganic metal ions or clusters connected to organic ligands. Shown are two such materials, HKUST-1 and MIL-100(Fe). An artificial intelligence technique -- machine learning -- is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing. Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability.

Capacitors, given their high energy output and recharging speed, could play a major role in powering the machines of the future, from electric cars to cell phones. But the biggest hurdle for these energy storage devices is that they store much less energy than a battery of similar size. Researchers at Georgia Institute of Technology are tackling that problem in a novel way, using machine learning to ultimately find ways to build more capable capacitors. The method, which was described in February 18 in the journal npj Computational Materials and sponsored by the U.S. Office of Naval Research, involves teaching a computer to analyze at an atomic level two materials that make up some capacitors: aluminum and polyethylene. The researchers focused on finding a way to more quickly analyze the electronic structure of those materials, looking for features that could affect performance.

The University of Connecticut is one of several leading research institutions collaborating with the Toyota Research Institute to accelerate the design and discovery of advanced materials using artificial intelligence and machine learning. The Toyota Research Institute (TRI) announced March 30 that it is investing $35 million to support the initiative over the next four years in an effort to revolutionize materials science and identify new advanced battery materials and fuel cell catalysts that can power future zero-emissions and carbon-neutral vehicles. It is extremely likely there are new and potentially much better functional polymers out there waiting to be discovered. Our goal is to accelerate the discovery process by using virtual screening methods … so that potential new polymers may be identified before they are made. Ramprasad's lab will work to identify new polymers using quantum mechanical computations and data-driven machine learning.