Periodic Reporting for period 1 - AI-TranspWood (AI-driven multiscale methodology to develop Transparent Wood as sustainable functional material)
Période du rapport: 2024-01-01 au 2025-06-30
The AI-TranspWood project is dedicated to advancing the development of Transparent Wood (TW) as an innovative sustainable and functional material for various industrial applications. TW has attracted significant scientific and industrial interest since its discovery and initial morphological characterization by S. Fink in 1992, and the more recent research started in 2015, independently, at KTH Royal Institute of Technology (Sweden) and at the University of Maryland (USA). The unique microstructure of TW, derived from that of natural wood, results in a composite that is lightweight and highly transparent to visible light. Through subsequent treatments, including chemical modification, monomer(s) infiltration, and functionalization, TW can achieve desirable properties for high-tech applications in such sectors as construction, automotive, electronics, and furniture.
Led by VTT Technical Research Centre of Finland Ltd. and supported by a broad European consortium of research institutions and industry partners, AI-TranspWood integrates advanced computational tools, experimental methods, and user-oriented design solutions. A central objective of AI-TranspWood is to demonstrate how artificial intelligence (AI)-enhanced numerical models, spanning from atomistic to continuum scales, can accelerate the innovation of new materials, particularly transparent wood, enabling it to replace less sustainable materials like plastics and glass. By harnessing AI, the project aims to establish a multiscale, AI-driven methodology to design and optimize TW within the Safe and Sustainable by Design (SSbD) framework. In AI-TranspWood, SSbD is used for virtual screening of polymers and chemicals, including their bio-based alternatives, using data from computational models and assessments to identify safety, environmental, and cost-effective solutions for production of TW and other wood-based composites.
A recent report estimates that the global TW industry will reach $208.1 million by 2031, with a compound annual growth rate (CAGR) of 9.0% from 2022 to 2031. Ultimately, the project aspires to make the production and optimization of safe, sustainable TW materials more efficient, supporting the transition to greener industrial practices and contributing to the global demand for low-impact, high-performance materials.
Led by VTT Technical Research Centre of Finland Ltd. and supported by a broad European consortium of research institutions and industry partners, AI-TranspWood integrates advanced computational tools, experimental methods, and user-oriented design solutions. A central objective of AI-TranspWood is to demonstrate how artificial intelligence (AI)-enhanced numerical models, spanning from atomistic to continuum scales, can accelerate the innovation of new materials, particularly transparent wood, enabling it to replace less sustainable materials like plastics and glass. By harnessing AI, the project aims to establish a multiscale, AI-driven methodology to design and optimize TW within the Safe and Sustainable by Design (SSbD) framework. In AI-TranspWood, SSbD is used for virtual screening of polymers and chemicals, including their bio-based alternatives, using data from computational models and assessments to identify safety, environmental, and cost-effective solutions for production of TW and other wood-based composites.
A recent report estimates that the global TW industry will reach $208.1 million by 2031, with a compound annual growth rate (CAGR) of 9.0% from 2022 to 2031. Ultimately, the project aspires to make the production and optimization of safe, sustainable TW materials more efficient, supporting the transition to greener industrial practices and contributing to the global demand for low-impact, high-performance materials.
Physics-based computational models were created to build an innovative AI-driven multiscale materials modeling software, assisted by characterization facilities, for development of functional TW composites:
• Several modelling approaches were developed to address the multi-scale complexity of the TW materials. At KTH, a specialized approach using distortion maps was developed to simulate three-dimensional microstructures of orthotropic wood-based composites, including both hardwoods like birch and softwoods like spruce.
• At TU Wien, the physics-driven modelling efforts organised were dedicated to constructing robust computational frameworks that predict the mechanical, thermal, and optical properties of transparent wood and related biocomposites. Various advanced techniques were applied, namely: continuum micromechanics-based homogenization, numerical simulations such as Finite Element Analysis with phase field methods to capture complex behaviours, and computational ray tracing to simulate how light passes through the material and interacts with it.
• At the atomistic level, KTH advanced a molecular dynamics (MD) approach focused on homogeneous and defect-free material surfaces. These simulations quantified the compatibility between the material and liquid monomers, producing values not dependent on structural variances.
• Additionally, at VTT, a molecular dynamics-based workflow was constructed to determine the viscosity of infiltrating substances at the atomic scale, which was then automated using the AiiDA workflow manager.
• At the macroscale, VTT developed a finite element (FE) model of the infiltration process, leveraging the monomer-surface compatibility data from KTH’s MD simulations and viscosity values modelled at VTT. Practical experiments, including the delignification and infiltration of birch, balsa, and spruce wood samples, were executed at UNISS. In addition, the complex viscosity values of the infiltrated monomers were measured by POLITO at different temperatures.
• Further, at VTT a simulation workflow using the OpenLB framework was implemented to model the permeability of wood structures. This system was designed to incorporate distortion-map-based, realistic wood geometries and was evaluated under different strategies for handling capillarity.
• At Aalto, two main surrogate models that simplify complex computational simulations were built: one replicated KTH’s birch microstructure generator and another mimicked TU Wien’s biocomposite stiffness model. Extensive datasets, created using generators, enabled broad exploration of input parameters. The surrogate models were implemented in PyTorch and TensorFlow.
Predictive computational LCA models and software, based on the Safe and Sustainable by Design concept were created:
• The initial efforts to integrate AI-driven multiscale material models with life cycle assessment (LCA) tools for the safe and sustainable development (SSbD) of TW composites have focused on building a structured workflow and screening models thank to the joint effort of VTT, UNISS, AIMPLAS, POLITO, KTH, and industrial partners.
• A first screening LCA model for the TW manufacturing was developed using the SULCA software. This early model will serve as a baseline for future integration in the Modeling Factory. The model assimilates data such as energy consumption and process performance indicators, linking them to LCA outcomes. In addition, material properties generated from multiscale modelling (e.g. transparency, mechanical strength) are mapped to the use phase, enabling estimation of the material’s life span in different applications.
• The screening LCA model, constructed from lab-scale processing data provided by project partners, enabled a comparative assessment of different delignification and lignin modification methods throughout the manufacturing phase.
• Several modelling approaches were developed to address the multi-scale complexity of the TW materials. At KTH, a specialized approach using distortion maps was developed to simulate three-dimensional microstructures of orthotropic wood-based composites, including both hardwoods like birch and softwoods like spruce.
• At TU Wien, the physics-driven modelling efforts organised were dedicated to constructing robust computational frameworks that predict the mechanical, thermal, and optical properties of transparent wood and related biocomposites. Various advanced techniques were applied, namely: continuum micromechanics-based homogenization, numerical simulations such as Finite Element Analysis with phase field methods to capture complex behaviours, and computational ray tracing to simulate how light passes through the material and interacts with it.
• At the atomistic level, KTH advanced a molecular dynamics (MD) approach focused on homogeneous and defect-free material surfaces. These simulations quantified the compatibility between the material and liquid monomers, producing values not dependent on structural variances.
• Additionally, at VTT, a molecular dynamics-based workflow was constructed to determine the viscosity of infiltrating substances at the atomic scale, which was then automated using the AiiDA workflow manager.
• At the macroscale, VTT developed a finite element (FE) model of the infiltration process, leveraging the monomer-surface compatibility data from KTH’s MD simulations and viscosity values modelled at VTT. Practical experiments, including the delignification and infiltration of birch, balsa, and spruce wood samples, were executed at UNISS. In addition, the complex viscosity values of the infiltrated monomers were measured by POLITO at different temperatures.
• Further, at VTT a simulation workflow using the OpenLB framework was implemented to model the permeability of wood structures. This system was designed to incorporate distortion-map-based, realistic wood geometries and was evaluated under different strategies for handling capillarity.
• At Aalto, two main surrogate models that simplify complex computational simulations were built: one replicated KTH’s birch microstructure generator and another mimicked TU Wien’s biocomposite stiffness model. Extensive datasets, created using generators, enabled broad exploration of input parameters. The surrogate models were implemented in PyTorch and TensorFlow.
Predictive computational LCA models and software, based on the Safe and Sustainable by Design concept were created:
• The initial efforts to integrate AI-driven multiscale material models with life cycle assessment (LCA) tools for the safe and sustainable development (SSbD) of TW composites have focused on building a structured workflow and screening models thank to the joint effort of VTT, UNISS, AIMPLAS, POLITO, KTH, and industrial partners.
• A first screening LCA model for the TW manufacturing was developed using the SULCA software. This early model will serve as a baseline for future integration in the Modeling Factory. The model assimilates data such as energy consumption and process performance indicators, linking them to LCA outcomes. In addition, material properties generated from multiscale modelling (e.g. transparency, mechanical strength) are mapped to the use phase, enabling estimation of the material’s life span in different applications.
• The screening LCA model, constructed from lab-scale processing data provided by project partners, enabled a comparative assessment of different delignification and lignin modification methods throughout the manufacturing phase.
We have developed and provided novel advanced, AI-powered multiscale modeling tools that can accurately forecast the physical, chemical, and thermo-mechanical characteristics of TW composites, considering their formulation, modifications, and manufacturing methods.
Moreover, we have implemented the Safe and Sustainable by Design (SSbD) framework for TW composites, highlighting their strong promise not only as innovative materials for targeted uses like construction, automotive, electronics, and furniture, but also as a foundation for an emerging eco-friendly market intended to rival those reliant on fossil-based products.
Finally, we have broadened the understanding of TW composites and their capabilities within both academic and industrial circles, thereby improving their performance, addressing current challenges, and supporting their adoption and utilization across various key industries.
Moreover, we have implemented the Safe and Sustainable by Design (SSbD) framework for TW composites, highlighting their strong promise not only as innovative materials for targeted uses like construction, automotive, electronics, and furniture, but also as a foundation for an emerging eco-friendly market intended to rival those reliant on fossil-based products.
Finally, we have broadened the understanding of TW composites and their capabilities within both academic and industrial circles, thereby improving their performance, addressing current challenges, and supporting their adoption and utilization across various key industries.