Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.
We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the classic hidden Markov model with the contextual representation power of pretrained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach (CHMM-ALT). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.
A single-step, template-free aerosol chemical vapor deposition (ACVD) method is demonstrated to grow well-aligned SnO 2 nanocolumn arrays. The ACVD system parameters, which control thin film morphologies, were systematically explored to gain a qualitative understanding of nanocolumn growth mechanisms. Key growth variables include feed rates, substrate temperature, and deposition time. System dynamics relating synthesis variables to aerosol characteristics and processes (collision and sintering) are elucidated. By adjusting system parameters, control of the aspect ratio, height, and crystal structure of columns is demonstrated. A self-catalyzed (SnO 2 particles) vapor-solid (VS) growth mechanism, whereby a vapor-particle deposition regime results in the formation of nanocrystals that act as nucleation sites for the preferential formation and growth of nanocolumns, is proposed and supported. Density functional theory (DFT) calculations indicate that the preferential orientation of thin films is a function of the system redox conditions, further supporting the proposed VS growth mechanism. When taken together, these results provide quantitative insight into the growth mechanismIJs) of SnO 2 nanocolumn thin films via ACVD, which is critical for engineering these, and other, nanostructured films for direct incorporation into functional devices.
Flexible
polymer dielectrics tolerant to electric field and temperature
extremes are urgently needed for a spectrum of electrical and electronic
applications. Given the complexity of the dielectric breakdown mechanism
and the vast chemical space of polymers, the discovery of suitable
candidates is nontrivial. We have laid the foundation for a systematic
search of the polymer chemical space, which starts with “gold-standard”
experimental measurements and data on the temperature-dependent breakdown
strength (E
bd) for a benchmark set of
commercial dielectric polymer films. Phenomenological guidelines are
derived from this data set on easily accessible properties (or “proxies”)
that are correlated with E
bd. Screening
criteria based on these proxy properties (e.g., band gap, charge injection
barrier, and cohesive energy density) and other necessary characteristics
(e.g., a high glass transition temperature to maintain the thermal
stability and a high dielectric constant for high energy density)
were then setup. These criteria, along with machine learning models
of these properties, were used to screen polymers candidates from
a candidate list of more than 13 000 previously synthesized
polymers, followed by experimental validation of some of the screened
candidates. These efforts have led to the creation of a consistent
and high-quality data set of temperature-dependent E
bd, and the identification of screening criteria, chemical
design rules, and a list of optimal polymer candidates for high-temperature
and high-energy-density capacitor applications, thus demonstrating
the power of an integrated and informatics-based philosophy for rational
materials design.
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