A5058861416- Conducting polymers and applications
- Machine Learning in Materials Science
- Organic Electronics and Photovoltaics
- Liquid Crystal Research Advancements
- Multimodal Machine Learning Applications
- Polymer crystallization and properties
- Computational Drug Discovery Methods
- Time Series Analysis and Forecasting
- Advanced Sensor and Energy Harvesting Materials
- Cell Image Analysis Techniques
Corning (United States)
2021
Stanford University
2017-2018
Abstract Nonconjugated segments in polymer semiconductors have been utilized to improve the processability of semiconducting polymers. Recently, several reports described improvement stretchability by incorporating nonconjugated spacers. However, effect relative flexibility such conjugation breakers on mechanical and electrical properties has not yet studied systematically. Here, with different chain length rigidity are incorporated into backbone diketopyrrolopyrrole‐based semiconductors....
Conjugated polymers are the key material in thin-film organic optoelectronic devices due to versatility of these molecules combined with their semiconducting properties. A molecular-scale understanding conjugated is important optimization morphology. We examine solution-phase behavior isoindigo-based donor–acceptor polymer single chains various chain lengths using atomistic molecular dynamics simulations. Our simulations elucidate transition from a rod-like coil-like conformation an analysis...
Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure-property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) QSPR develop machine learning models perform high fidelity prediction glass transition temperature (Tg), melting (Tm), density (ρ), tensile...
Phase segregation, the process by which components of a binary mixture spontaneously separate, is key in evolution and design many chemical, mechanical, biological systems. In this work, we present data-driven approach for learning, modeling, prediction phase segregation. A direct mapping between an initially dispersed, immiscible fluid equilibrium concentration field learned conditional generative convolutional neural networks. Concentration predictions deep learning model conserve...
In the domain of scientific imaging, interpreting visual data often demands an intricate combination human expertise and deep comprehension subject materials. This study presents a novel methodology to linguistically emulate subsequently evaluate human-like interactions with Scanning Electron Microscopy (SEM) images, specifically glass Leveraging multimodal learning framework, our approach distills insights from both textual harvested peer-reviewed articles, further augmented by capabilities...
The phase separation between donor and acceptor molecules within the active layer of an organic solar cells dictates morphology hence is key to recombination rate ultimately performance cell. Molecular dynamics (MD) simulation a suitable technique understand this phenomenon; however, conventional all-atom MD simulations cannot reach appropriate length time scales compare with macroscopic observation. Even many available coarse-grained models, it difficult these scales. Therefore, we...