Jurģis Ruža

ORCID: 0009-0008-7151-1250
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About
Contact & Profiles
Research Areas
  • Fuel Cells and Related Materials
  • Conducting polymers and applications
  • Advanced Sensor and Energy Harvesting Materials
  • Electrochemical Analysis and Applications
  • Advanced Photocatalysis Techniques
  • Advanced Battery Materials and Technologies
  • Machine Learning in Materials Science
  • Electrospun Nanofibers in Biomedical Applications
  • Tactile and Sensory Interactions
  • Advanced Chemical Sensor Technologies
  • Iron oxide chemistry and applications
  • Copper-based nanomaterials and applications
  • Polymer Science and PVC
  • Chemistry and Chemical Engineering
  • Polymer crystallization and properties
  • Ionic liquids properties and applications
  • Inorganic and Organometallic Chemistry
  • Green IT and Sustainability
  • 3D Printing in Biomedical Research
  • Phase Equilibria and Thermodynamics
  • Advanced Materials and Mechanics

Massachusetts Institute of Technology
2020-2025

École Polytechnique Fédérale de Lausanne
2020

Riga Technical University
2017-2018

Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high SPEs, we developed a chemistry-informed machine learning model that accurately predicts SPEs. The was trained on SPE data hundreds experimental...

10.1021/acscentsci.2c01123 article EN cc-by ACS Central Science 2023-01-23

Solid polymer electrolytes are an exciting solution for safe and stable solid lithium electrode battery systems but hindered by low ionic conductivity transference. All-atom molecular dynamics simulation has become invaluable tool to probe diffusion mechanisms accelerate the discovery of promising chemistries. Because their computational cost despite approximate nature, only classical interatomic potentials can access time length scales appropriate statistics kinetics. Machine learning (ML)...

10.1021/acs.chemmater.4c02529 article EN Chemistry of Materials 2025-02-23

Polymer electrolytes may play a crucial role in the development of safe, efficient energy-dense batteries thanks to their unique ability facilitate ion transport while maintaining structural stability. However, experimental discovery is limited by complexity synthesizing and testing new monomer polymer chemistries. In this study, we benchmark molecular dynamics (MD) simulations with Class 1 force fields model properties high-throughput screening setting. By systematically comparing...

10.26434/chemrxiv-2025-q4822 preprint EN cc-by 2025-02-28

Solid polymer electrolytes are a promising class of materials to enable next-generation Li-based batteries. They offer highly tunable properties, scalable processing conditions, and increased safety. However, current solid do not have sufficient ionic conductivity for room-temperature battery applications. The discovery novel polymers the optimization polymer-salt formulations with high critical bottlenecks in developing new polymer-based solid-state Programmable laboratories driven by...

10.26434/chemrxiv-2025-2cjbg preprint EN 2025-04-07

Polymer electrolytes may play a crucial role in the development of safe, efficient energy-dense batteries thanks to their unique ability facilitate ion transport while maintaining structural stability. However, experimental discovery is limited by complexity synthesizing and testing new monomer polymer chemistries. In this study, we benchmark molecular dynamics (MD) simulations with Class 1 force fields model properties high-throughput screening setting. By systematically comparing...

10.26434/chemrxiv-2025-q4822-v2 preprint EN 2025-04-10

Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in time scales atomistic ensemble motion. Coarse-grained models are therefore used place costly simulations, allowing simulation longer larger systems. Nevertheless, constructing many-body potential mean force that defines structure dynamics coarse-grained system be complicated...

10.1063/5.0022431 article EN publisher-specific-oa The Journal of Chemical Physics 2020-10-22

Abstract Biodegradable polymers are increasingly employed at the heart of therapeutic devices. Particularly in form thin and elongated fibers, they offer an effective strategy for controlled release a variety biomedical configurations such as sutures, scaffolds, wound dressings, surgical or imaging probes, smart textiles. So far however, fabrication fiber‐based drug delivery systems has been unable to fulfill significant requirements medicated fibers multifunctionality, adequate mechanical...

10.1002/adfm.201910283 article EN Advanced Functional Materials 2020-02-14

10.1016/0032-3950(62)90162-4 article EN Polymer Science U S S R 1962-01-01

Solid polymer electrolytes are an exciting solution for safe and stable solid lithium electrode battery systems but hindered by low ionic conductivity transference. All-atom molecular dynamics simulation has become invaluable tool to probe diffusion mechanisms accelerate the discovery of promising chemistries. Because their computational cost despite approximate nature, only classical interatomic potentials can access time length scales appropriate statistics kinetics. Machine learning (ML)...

10.26434/chemrxiv-2024-8r8j1 preprint EN 2024-09-10

Solid polymer electrolytes are an exciting solution for safe and stable solid lithium electrode battery systems but hindered by low ionic conductivity transference. All-atom molecular dynamics simulation has become invaluable tool to probe diffusion mechanisms accelerate the discovery of promising chemistries. Because their computational cost despite approximate nature, only classical interatomic potentials can access time length scales appropriate statistics kinetics. Machine learning (ML)...

10.26434/chemrxiv-2024-8r8j1-v2 preprint EN 2024-09-11
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