Periodic Reporting for period 2 - DYNAPOL (Modeling approaches toward bioinspired dynamic materials)
Okres sprawozdawczy: 2021-05-01 do 2022-10-31
Nature uses the principle of self-assembly to build materials with fascinating properties, such as the ability to self-heal, to dynamically adapt and reconfigure, or to respond to specific stimuli in controlled way. Microtubules, protein filaments, cellular membranes, for example, are supramolecular polymers made of fundamental molecular building-blocks, or monomers (e.g. proteins, lipids, etc.), which recognize each other and self-assemble into ordered structures in determined conditions with unique fidelity and precision. The dynamic character of such assemblies makes it possible to embed them in complex systems where they are in continuous communication with the external environment, expressing complex functionalities that are impossible for traditional technological materials such as e.g. metals or covalent polymers.
This ERC project “Modeling Approaches Toward Bioinspired Dynamic Materials (DYNAPOL)” aims to answer key, intriguing questions, such as:
- Is it possible to impart similar bioinspired behaviors into synthetic materials build via self-assembly?
- And if yes, how?
The ability to conceive and design new types of materials has determined the development of humanity from the stone, bronze and iron ages to our current world, dominated by electronic materials and semiconductors. This DYNAPOL project will explore new routes to design new types of artificial materials with fascinating bioinspired properties, reminiscent to those of living systems. This would be a breakthrough in many fields, from biomedicine, to the chemical industry, to the design of new types of advanced materials for futuristic technological applications.
The DYNAPOL project will use a concerted computational approach to learn how to control the bioinspired dynamic properties of supramolecular assemblies. Multiscale molecular models will allow to study the supramolecular structure of various types of self-assembled materials on multiple scales (Objective 1). Advanced computational simulation approaches will then allow to study the innate supramolecular dynamics of these materials at very high resolution (Objective 2). In silico experiments will allow to study bioinspired properties such as the ability of various supramolecular materials to self-heal, to adapt, or to reconfigure dynamically in response to specific stimuli at high resolution (Objective 3). Machine learning approaches will finally allow identifying from the large amount of data extracted from the molecular simulations key features in the constitutive monomers and the determinant factors that control the structure, dynamics, and dynamic properties of a supramolecular material (Objective 4).
This research will produce unprecedented insight and fundamental models for the rational design of new types of artificial materials with controllable dynamic bioinspired properties.
This ERC project “Modeling Approaches Toward Bioinspired Dynamic Materials (DYNAPOL)” aims to answer key, intriguing questions, such as:
- Is it possible to impart similar bioinspired behaviors into synthetic materials build via self-assembly?
- And if yes, how?
The ability to conceive and design new types of materials has determined the development of humanity from the stone, bronze and iron ages to our current world, dominated by electronic materials and semiconductors. This DYNAPOL project will explore new routes to design new types of artificial materials with fascinating bioinspired properties, reminiscent to those of living systems. This would be a breakthrough in many fields, from biomedicine, to the chemical industry, to the design of new types of advanced materials for futuristic technological applications.
The DYNAPOL project will use a concerted computational approach to learn how to control the bioinspired dynamic properties of supramolecular assemblies. Multiscale molecular models will allow to study the supramolecular structure of various types of self-assembled materials on multiple scales (Objective 1). Advanced computational simulation approaches will then allow to study the innate supramolecular dynamics of these materials at very high resolution (Objective 2). In silico experiments will allow to study bioinspired properties such as the ability of various supramolecular materials to self-heal, to adapt, or to reconfigure dynamically in response to specific stimuli at high resolution (Objective 3). Machine learning approaches will finally allow identifying from the large amount of data extracted from the molecular simulations key features in the constitutive monomers and the determinant factors that control the structure, dynamics, and dynamic properties of a supramolecular material (Objective 4).
This research will produce unprecedented insight and fundamental models for the rational design of new types of artificial materials with controllable dynamic bioinspired properties.
The work performed in the first 30 months of DYNAPOL is summarized below.
Objective 1) Multiscale molecular models for studying supramolecular structures. Achieved results:
- Development of an automatic optimization software allowing to optimize molecular models with a considerable precision (ACS Omega 2020, 5, 32823; J. Chem. Phys. 2022, 156, 024801).
- Development of accurate multiscale molecular models allowing to simulate very complex self-assembled structures (Nature 2020, 583, 400; Nat. Chem. 2022, 14, 507).
Objective 2) Study of the supramolecular dynamics via advanced simulations. Achieved results:
- Linking the intrinsic dynamics of supramolecular polymers to their dynamic tendency to mix into supramolecular copolymers (JACS 2020, 142, 7606).
- Study of supramolecular polymers mixing to form axial block supramolecular copolymers (JACS 2020, 142, 11528).
- Obtaining a general understanding of the monomer exchange dynamics (ACS Nano 2021, 15, 14229), unveiling the complex dynamic behavior characterizing even the simplest supramolecular systems (Nat. Commun. 2022, 13, 2162).
Objective 3) Submolecular-resolution study of stimuli-responsiveness. Achieved results:
- Modeling host-guest systems responsive to light (JACS 2020, 142, 9792; JACS 2020, 142, 14557).
- Modeling bioinspired fuel-driven supramolecular polymers (Chem.SystemsChem. 2021, 3, e2000038).
- Modeling stimuli-responsive nanoparticles/micelles (ACS Nano 2021, 15, 16149).
Objective 4) Machine learning of monomer-assemblies relationships. Achieved results:
- Abstract comparison of order/disorder in self-assembled materials (Nature Commun. 2021, 12, 3134; JPCB 2020, 124, 589).
- Development of order/disorder metrics to classify molecular models of soft dynamic assemblies (J. Phys. Chem. B 2021, 125, 7785).
Objective 1) Multiscale molecular models for studying supramolecular structures. Achieved results:
- Development of an automatic optimization software allowing to optimize molecular models with a considerable precision (ACS Omega 2020, 5, 32823; J. Chem. Phys. 2022, 156, 024801).
- Development of accurate multiscale molecular models allowing to simulate very complex self-assembled structures (Nature 2020, 583, 400; Nat. Chem. 2022, 14, 507).
Objective 2) Study of the supramolecular dynamics via advanced simulations. Achieved results:
- Linking the intrinsic dynamics of supramolecular polymers to their dynamic tendency to mix into supramolecular copolymers (JACS 2020, 142, 7606).
- Study of supramolecular polymers mixing to form axial block supramolecular copolymers (JACS 2020, 142, 11528).
- Obtaining a general understanding of the monomer exchange dynamics (ACS Nano 2021, 15, 14229), unveiling the complex dynamic behavior characterizing even the simplest supramolecular systems (Nat. Commun. 2022, 13, 2162).
Objective 3) Submolecular-resolution study of stimuli-responsiveness. Achieved results:
- Modeling host-guest systems responsive to light (JACS 2020, 142, 9792; JACS 2020, 142, 14557).
- Modeling bioinspired fuel-driven supramolecular polymers (Chem.SystemsChem. 2021, 3, e2000038).
- Modeling stimuli-responsive nanoparticles/micelles (ACS Nano 2021, 15, 16149).
Objective 4) Machine learning of monomer-assemblies relationships. Achieved results:
- Abstract comparison of order/disorder in self-assembled materials (Nature Commun. 2021, 12, 3134; JPCB 2020, 124, 589).
- Development of order/disorder metrics to classify molecular models of soft dynamic assemblies (J. Phys. Chem. B 2021, 125, 7785).
The innovative character of the work performed during the first 30 months of DYNAPOL is summarized below.
OBJ 1)
Beyond the state of the art:
- Development of molecular models (from atomistic resolution and scale-up), providing a rich characterization of complex hierarchical assemblies (Nature 2020, 583, 400; Nat. Chem. 2022, 14, 507).
- Development of a software automatically optimizing molecular models, reducing the burden of human-based tuning and allowing to optimize a variety of models (J. Chem. Phys. 2022, 156, 024801).
Expected results by the end of the project:
- Enrich the diversity of supramolecular structures that will be simulated
- Improvement of our automatic optimization platform
OBJ 2)
Beyond the state of the art:
- Using advanced simulations to study supramolecular dynamics at submolecular resolution in complex assemblies is innovative and has high potential (JACS 2020, 142, 7606; JACS 2020, 142, 11528).
- Advanced simulations coupled with coarse-grained molecular models may allow to study collective behaviors emerging in complex systems system containing multiple assemblies (Nat. Commun. 2022, 13, 2162)
Expected results by the end of the project:
- Enrich the diversity of dynamic assemblies that will be simulated
- Study self-assembling systems as complex systems composed of many molecular entities, and study higher levels of dynamics
OBJ 3)
Beyond the state of the art:
- The study of the dynamic behavior of host-guest reactive systems at submolecular resolution has high potential (JACS 2020, 142, 9792).
- The submolecular resolution modeling of living, or fuel-regulated assemblies provide information on the behavior of the systems out-of-equilibrium (Chem.SystemsChem. 2021, 3, e2000038).
Expected results by the end of the project:
- Study of a variety of bioinspired properties, enlarging the diversity in terms of types of assemblies that will be studied and of stimuli
- Study of autonomous bioinspired behaviors such as chemotacticity (ACS Nano 2021, 15, 16149)
OBJ 4)
Beyond the state of the art:
- Comparing in abstract way dynamic assemblies based on their order/disorder and defects is a new, largely unexplored field (J. Phys. Chem. B 2020, 124, 589)
- The use of abstract data-driven metrics to classify dynamic assembled systems (J. Phys. Chem. B 2021, 125, 7785; ArXiv 2021, arXiv:2112.08044) is a key first step towards monomer-assembly relationships useful toward rational design.
Expected results by the end of the project:
- Classify a variety of soft dynamic assemblies & monomers integrating high-dimensional fingerprints, unsupervised clustering and abstract metrics representative of the structural-dynamic features of the assemblies
- Machine learning of monomer-assemblies structure-dynamics-property relationships
OBJ 1)
Beyond the state of the art:
- Development of molecular models (from atomistic resolution and scale-up), providing a rich characterization of complex hierarchical assemblies (Nature 2020, 583, 400; Nat. Chem. 2022, 14, 507).
- Development of a software automatically optimizing molecular models, reducing the burden of human-based tuning and allowing to optimize a variety of models (J. Chem. Phys. 2022, 156, 024801).
Expected results by the end of the project:
- Enrich the diversity of supramolecular structures that will be simulated
- Improvement of our automatic optimization platform
OBJ 2)
Beyond the state of the art:
- Using advanced simulations to study supramolecular dynamics at submolecular resolution in complex assemblies is innovative and has high potential (JACS 2020, 142, 7606; JACS 2020, 142, 11528).
- Advanced simulations coupled with coarse-grained molecular models may allow to study collective behaviors emerging in complex systems system containing multiple assemblies (Nat. Commun. 2022, 13, 2162)
Expected results by the end of the project:
- Enrich the diversity of dynamic assemblies that will be simulated
- Study self-assembling systems as complex systems composed of many molecular entities, and study higher levels of dynamics
OBJ 3)
Beyond the state of the art:
- The study of the dynamic behavior of host-guest reactive systems at submolecular resolution has high potential (JACS 2020, 142, 9792).
- The submolecular resolution modeling of living, or fuel-regulated assemblies provide information on the behavior of the systems out-of-equilibrium (Chem.SystemsChem. 2021, 3, e2000038).
Expected results by the end of the project:
- Study of a variety of bioinspired properties, enlarging the diversity in terms of types of assemblies that will be studied and of stimuli
- Study of autonomous bioinspired behaviors such as chemotacticity (ACS Nano 2021, 15, 16149)
OBJ 4)
Beyond the state of the art:
- Comparing in abstract way dynamic assemblies based on their order/disorder and defects is a new, largely unexplored field (J. Phys. Chem. B 2020, 124, 589)
- The use of abstract data-driven metrics to classify dynamic assembled systems (J. Phys. Chem. B 2021, 125, 7785; ArXiv 2021, arXiv:2112.08044) is a key first step towards monomer-assembly relationships useful toward rational design.
Expected results by the end of the project:
- Classify a variety of soft dynamic assemblies & monomers integrating high-dimensional fingerprints, unsupervised clustering and abstract metrics representative of the structural-dynamic features of the assemblies
- Machine learning of monomer-assemblies structure-dynamics-property relationships