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We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified. Our approach demonstrates a potential reduction in discovery time by approximately 75 %, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to extract data from more than 3300 papers which is ~5 times larger than similar data sets reported by others. We also trained machine learning models to predict the power conversion efficiency and used our model to identify promising donor-acceptor combinations that are as yet unreported. We thus demonstrate a workflow that goes from published literature to extracted material property data which in turn is used to obtain data-driven insights. Our insights include active learning strategies that can simultaneously optimize the material system and train strong predictive models of material properties. This work provides a valuable framework for research in material science.

Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to specific structured formats, which is crucial in applications like named entity recognition (NER) or relation extraction (RE). To address this issue, this paper introduces an efficient method, G&O, to enhance their structured text generation capabilities. It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses. Subsequently, LLMs are asked to organize the output into the desired structure, using the intermediate responses as context. G&O effectively separates the generation of content from the structuring process, reducing the pressure of completing two orthogonal tasks simultaneously. Tested on zero-shot NER and RE, the results indicate a significant improvement in LLM performance with minimal additional efforts. This straightforward and adaptable prompting technique can also be combined with other strategies, like self-consistency, to further elevate LLM capabilities in various structured text generation tasks., Comment: 15 pages, 5 figures, 5 tables

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no existing SciIE datasets for polymer materials, which is an important class of materials used ubiquitously in our daily lives. To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer materials. POLYIE is curated from 146 full-length polymer scholarly articles, which are annotated with different named entities (i.e., materials, properties, values, conditions) as well as their N-ary relations by domain experts. POLYIE presents several unique challenges due to diverse lexical formats of entities, ambiguity between entities, and variable-length relations. We evaluate state-of-the-art named entity extraction and relation extraction models on POLYIE, analyze their strengths and weaknesses, and highlight some difficult cases for these models. To the best of our knowledge, POLYIE is the first SciIE benchmark for polymer materials, and we hope it will lead to more research efforts from the community on this challenging task. Our code and data are available on: https://github.com/jerry3027/PolyIE., Comment: Work in progress

Recent works have shown the promise of learning pre-trained models for 3D molecular representation. However, existing pre-training models focus predominantly on equilibrium data and largely overlook off-equilibrium conformations. It is challenging to extend these methods to off-equilibrium data because their training objective relies on assumptions of conformations being the local energy minima. We address this gap by proposing a force-centric pretraining model for 3D molecular conformations covering both equilibrium and off-equilibrium data. For off-equilibrium data, our model learns directly from their atomic forces. For equilibrium data, we introduce zero-force regularization and forced-based denoising techniques to approximate near-equilibrium forces. We obtain a unified pre-trained model for 3D molecular representation with over 15 million diverse conformations. Experiments show that, with our pre-training objective, we increase forces accuracy by around 3 times compared to the un-pre-trained Equivariant Transformer model. By incorporating regularizations on equilibrium data, we solved the problem of unstable MD simulations in vanilla Equivariant Transformers, achieving state-of-the-art simulation performance with 2.45 times faster inference time than NequIP. As a powerful molecular encoder, our pre-trained model achieves on-par performance with state-of-the-art property prediction tasks.

Polymer simulation with both accuracy and efficiency is a challenging task. Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields. However, existing ML force fields are usually limited to single-molecule settings, and their simulations are not robust enough. In this paper, we present PolyGET, a new framework for Polymer Forcefields with Generalizable Equivariant Transformers. PolyGET is designed to capture complex quantum interactions between atoms and generalize across various polymer families, using a deep learning model called Equivariant Transformers. We propose a new training paradigm that focuses exclusively on optimizing forces, which is different from existing methods that jointly optimize forces and energy. This simple force-centric objective function avoids competing objectives between energy and forces, thereby allowing for learning a unified forcefield ML model over different polymer families. We evaluated PolyGET on a large-scale dataset of 24 distinct polymer types and demonstrated state-of-the-art performance in force accuracy and robust MD simulations. Furthermore, PolyGET can simulate large polymers with high fidelity to the reference ab initio DFT method while being able to generalize to unseen polymers.

Objetivo: Diseñar un proceso circular e inteligente para el aprovechamiento de desechos de la industria pesquera de la ciudad de Mar del Plata (República Argentina) a través del cultivo de hongos. Metodología: Se adopta una metodología cuantitativa, de tipo experimental, a través del cultivode inóculos de gírgola (Pleutorus sp) y reishi (Ganoderma lucidum) utilizando un sustrato conformado por una proporción de desechos de anchoíta (Engraulis anchoita) con alto contenido de cloruro de sodio y residuo vegetal (rico en ligninay celulosa). Resultados: En el estudio experimental se obtuvieron resultados sobre la selección, acondicionamiento y composición de los insumos para el diseño del proceso de producción fúngica. En la incorporación de insumos, se utilizaron los inóculos secundarios de gírgola (Pleutorus sp) y reishi (Ganoderma lucidum) y el sustrato (paja de pino y desechode anchoíta). La composición del sustrato se determinó en un 70% de paja de pino como fuente de lignina y celulosa y en un 30% de desecho de anchoíta. El acondicionamiento del sustrato para el desarrollo del cultivo precisó de una humidificación de los residuos vegetales, un secado de los desechos animales yel posterior proceso de esterilización. La supervivencia del micelio en un sustrato con desechos animales con alto contenido de sal se presenta similar para las dos especies de hongos consideradas, por lo cual, para que prospere el cultivo se requieren valores de humedad inferiores a 49%. Trazabilidad genuina de los insumos (desechos de anchoíta y hongos) y del proceso de desarrollo -micelio- hasta la obtención, a posteriori, de las setas. Limitaciones: El experimento se limita a productos en salazón de la especie Engraulis anchoíta yaún está en estudio la calidad de los productos obtenidos después de los procesos de incubación y fructificación, así como el reaprovechamiento del sustrato. Originalidad: El aprovechamiento de residuos de la industria pesquera conservera, con alto contenido de cl, Fil: Zanfrillo, Alicia Inés. Universidad Nacional de Mar del Plata. Facultad de Ciencias Económicas y Sociales; Argentina., Fil: Maggiore, Marina. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Campins, Macarena. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Rampi, Mariana. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Darago, Andrés. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina.

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.

The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature. We used natural language processing (NLP) 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 when used as the encoder for text. 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 through a web platform at https://polymerscholar.org which can be used to locate material property data recorded in abstracts conveniently. This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with a complete set of extracted material property information.

La fungicultura genera tanto desarrollo económico como beneficios en el medio ambiente. La importancia de esta actividad radica en el reciclaje acelerado de subproductos, principalmente agrícolas, utilizados como sustrato de cultivo. Sin embargo, aún es desconocido el efecto que conlleva el uso de otros tipos de sustratos orgánicos, considerados contaminantes medioambientales, como lo son los desechos generados en la industria pesquera, en la producción de setas comestibles. El objetivo del trabajo fue realizar el cultivo de gírgolas (Pleurotus ostreatus) utilizando como parte del sustrato desechos de langostino, raya y anchoíta lavada y sin lavar. Para ello se elaboraron y acondicionaron cuatro sustratos, cada uno constituido con 80% de pellet de trigo y 20% del desecho determinado. Los mismos fueron inoculados con semillas de gírgola. Posteriormente se los colocó en una incubadora a 22ºC en oscuridad durante 20 días. Pasado el tiempo de incubación, el sustrato que presentó mayor crecimiento micelial fue el que estaba constituido por desecho de langostino, seguido por aquel integrado con restos de raya. En la anchoíta lavada y sin lavar no se observó crecimiento de micelio. En conclusión, es factible el reciclado de los desechos de la pesca mediante el cultivo de hongos., Fil: Maggiore, Marina A. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Rampi, Mariana G. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Campins, Macarena. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Carrasco, Lucas. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Zanfrillo, Alicia Inés. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina.

Objetivo: Diseñar un proceso circular e inteligente para el aprovechamiento de desechos de la industria pesquera de la ciudad de Mar del Plata (República Argentina) a través del cultivo de hongos. Metodología: Se adopta una metodología cuantitativa, de tipo experimental, a través del cultivode inóculos de gírgola (Pleutorus sp) y reishi (Ganoderma lucidum) utilizando un sustrato conformado por una proporción de desechos de anchoíta (Engraulis anchoita) con alto contenido de cloruro de sodio y residuo vegetal (rico en ligninay celulosa). Resultados: En el estudio experimental se obtuvieron resultados sobre la selección, acondicionamiento y composición de los insumos para el diseño del proceso de producción fúngica. En la incorporación de insumos, se utilizaron los inóculos secundarios de gírgola (Pleutorus sp) y reishi (Ganoderma lucidum) y el sustrato (paja de pino y desechode anchoíta). La composición del sustrato se determinó en un 70% de paja de pino como fuente de lignina y celulosa y en un 30% de desecho de anchoíta. El acondicionamiento del sustrato para el desarrollo del cultivo precisó de una humidificación de los residuos vegetales, un secado de los desechos animales yel posterior proceso de esterilización. La supervivencia del micelio en un sustrato con desechos animales con alto contenido de sal se presenta similar para las dos especies de hongos consideradas, por lo cual, para que prospere el cultivo se requieren valores de humedad inferiores a 49%. Trazabilidad genuina de los insumos (desechos de anchoíta y hongos) y del proceso de desarrollo -micelio- hasta la obtención, a posteriori, de las setas. Limitaciones: El experimento se limita a productos en salazón de la especie Engraulis anchoíta yaún está en estudio la calidad de los productos obtenidos después de los procesos de incubación y fructificación, así como el reaprovechamiento del sustrato. Originalidad: El aprovechamiento de residuos de la industria pesquera conservera, con alto contenido de cl, Fil: Zanfrillo, Alicia Inés. Universidad Nacional de Mar del Plata. Facultad de Ciencias Económicas y Sociales; Argentina., Fil: Maggiore, Marina. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Campins, Macarena. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Rampi, Mariana. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina., Fil: Darago, Andrés. Universidad Tecnológica Nacional. Facultad Regional Mar del Plata; Argentina.

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.

Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multi-task learning and meta-learning are proposed. A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data., Comment: This paper has been submitted for conference review

Objetivo: Investigar a prevalência e os fatores associados ao refluxo gastroesofágico em indivíduos de 0 a 33 anos de idade com deficiências do desenvolvimento. Métodos: 628 prontuários odontológicos foram analisados. Os casos de refluxo foram anotados baseados nos relatos dos responsáveis. Variáveis com valor de p menor do que 0.25 foram incluídas no modelo de regressão logística. Variáveis com valor de p menor do que 0.05 foram consideradas como significativas no modelo final. Resultados: ser respirador bucal, usar “outros medicamentos” e ter histórico de convulsões foram fatores associados ao refluxo gastroesofágico entre pacientes com deficiências de desenvolvimento.

The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the \emph{forward} problem. However, this approach is inefficient, and severely constrained by the candidates that human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the \emph{inverse} problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature \emph{and} high electric field, useful for critical structural, electrical and energy storage applications. This approach to learn from (and augment) human ingenuity is general, and can be extended to discover polymers with other targeted properties and performance measures.

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict the properties of new polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively, particularly when some property dataset sizes are small. Data pertaining to 36 different properties of over $13, 000$ polymers (corresponding to over $23,000$ data points) are coalesced and supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in specific property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform.

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform.

The mechanisms of electron transport in metal-insulator-metal junctions are incompletely understood. A metal-insulatormetal junction consisting of a self-assembled monolayer (SAM) supported on a mercury drop in mechanical contact with a SAM on a planar metal electrode has been developed as a test-bed with which to study electron transport through organic films. This review provides a summary of results intended to characterize this junction including: i) the determination of the electrical breakdown field of organic monolayers, ii) the determination of the tunneling decay constant for aliphatic and aromatic organic oligomers, and iii) the examination of molecular rectifier.