A5020243467- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Scientific Computing and Data Management
- Crystallization and Solubility Studies
- Advanced Memory and Neural Computing
- Conducting polymers and applications
- Modular Robots and Swarm Intelligence
- Crystallography and molecular interactions
- Advanced Sensor and Energy Harvesting Materials
- Innovative Microfluidic and Catalytic Techniques Innovation
- Computational Drug Discovery Methods
- Organic Electronics and Photovoltaics
- Power Line Communications and Noise
- Electronic and Structural Properties of Oxides
- Machine Learning in Bioinformatics
- Electrochemical sensors and biosensors
- Biomedical Text Mining and Ontologies
- Analytical Chemistry and Chromatography
- Advanced NMR Techniques and Applications
- Chemical Synthesis and Analysis
- Luminescence and Fluorescent Materials
- Fuel Cells and Related Materials
- Genomics and Phylogenetic Studies
- Optimization and Search Problems
- Advanced Wireless Communication Techniques
Argonne National Laboratory
2023-2025
University of Liverpool
2020-2024
University of Leeds
2023
University of York
2018-2020
Owing to the chemical pluripotency and viscoelastic nature of electronic polymers, polymer electronics have shown unique advances in many emerging applications such as skin-like electronics, large-area printed energy devices, neuromorphic computing but their development period is years-long. Recent advancements automation, robotics, learning algorithms led a growing number self-driving (autonomous) laboratories that begun revolutionize accelerated discovery materials. In this perspective, we...
Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing full potential poses a formidable challenge, as design has historically relied on iterative adjustments continuous inspiration from researchers. Traditional methods often struggle efficiently navigate vast chemical landscape. Herein, the application of artificial intelligence (AI), specifically machine learning (ML), needs be discussed...
Abstract The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently polymer solutions into thin films with specific remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore pathways for achieving high-conductivity, low-defect polymers films. Leveraging importance-guided Bayesian optimization, Polybot...
A conductive ladder polymer, designed and synthesized taking inspiration from the structure of polyaniline, exhibits exceptional stability under harsh conditions superior durability across numerous redox cycles in operating devices.
Abstract Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron are revolutionizing our understanding of materials across the spectrum physical sciences, from life sciences microelectronics. However, these facility instrument upgrades come with a significant increase in complexity. Driven by more exacting needs, instruments experiments become intricate each year. This increased operational complexity makes it ever...
Molecular set transformer is a deep learning architecture for scoring molecular pairs found in co-crystals, whilst tackling the class imbalance problem observed on datasets that include only successful synthetic attempts.
Automated platforms allow for rapid, detailed screening of chemical systems.
Advances in robotic automation, high-performance computing, and artificial intelligence encourage us to propose large, general-purpose science factories with the scale needed tackle large discovery problems support thousands of scientists.
Abstract The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay design processing conditions. However, screening optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time resources. To overcome this challenge, Polybot is developed—a robotic platform within self‐driving lab that efficiently produce field‐effect transistors (OFETs) from various via...
The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter concern data-driven approaches is lack negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose application one-class classification methodology as an effective tool tackling these limitations on design problems. This a concept based only well-defined class without counter examples. An extensive...
Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation- AI-enabled self-driving laboratories (SDLs) with the generality scale needed both tackle large discovery problems support thousands scientists. Science factories require modular hardware software that can be replicated for (re)configured many applications. To this end, we propose a prototype factory architecture...
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron are revolutionizing our understanding of materials across the spectrum physical sciences, from life sciences microelectronics. However, these facility instrument upgrades come with a significant increase in complexity. Driven by more exacting needs, instruments experiments become intricate each year. This increased operational complexity makes it ever challenging for...
K 3 coronene is computationally predicted and synthesised. Crystal structure solution reveals a highly disordered with evidence of local spin-singlet formation from magnetic property EPR measurements.
The Mellin transform (MT) method (MTM) is a of analytical evaluation definite integrals that utilizes key concepts complex analysis (CA). Although sometimes laborious, the algorithmic, stepwise character as well its vast field applications render it powerful and versatile. In particular, certain are presented in literature without proof (or with proofs ingenious nonintuitive) can be directly attacked standard manner. This true for three examples herein, all taken from advanced...