Periodic Reporting for period 5 - SOFTWATER (Soft Water: understanding what makes a fluid behave like water)
Okres sprawozdawczy: 2022-11-01 do 2024-03-31
Water, the most abundant and essential substance on Earth, remains one of the most enigmatic. Its behavior defies the norms of other liquids, with dozens of anomalies that scientists continue to explore. For instance, ice floats because it is less dense than liquid water—a property rarely observed in other substances. Understanding the unique properties of water is crucial, as it constitutes around half the volume of every living biological cell, is vital for industrial processes, and plays a central role in regulating Earth’s climate.
The SOFTWATER project sought to uncover the molecular origins of water’s exceptional characteristics using advanced computational techniques. By modeling water’s tetrahedral hydrogen bonds and systematically altering these models, the research team was able to isolate and study the factors responsible for water’s anomalies. This computational approach allowed the exploration of behaviors that are otherwise nearly impossible to observe experimentally.
One of the project’s key achievements was its ability to make the deeply cooled, metastable state of water—often called “No Man’s Land” due to its experimental inaccessibility—more accessible for study. This state is believed to hold the secrets to water’s unique properties but has been challenging to probe without crystallization occurring. The simulations enabled us to study this elusive state and identify the structural dynamics that contribute to water’s unusual behavior.
The implications of these findings are far-reaching. By deepening our understanding of water, the project contributes to more accurate climate modeling, where water plays a critical role in predicting weather patterns and understanding global changes. Furthermore, the ability to “tune” molecules to mimic water’s behavior could revolutionize industrial processes and lead to innovative applications in colloidal science, where insoluble particles are suspended in a medium.
The SOFTWATER project highlights the power of computational modeling in uncovering fundamental insights into material behavior and opens new avenues for research across disciplines.
The SOFTWATER project sought to uncover the molecular origins of water’s exceptional characteristics using advanced computational techniques. By modeling water’s tetrahedral hydrogen bonds and systematically altering these models, the research team was able to isolate and study the factors responsible for water’s anomalies. This computational approach allowed the exploration of behaviors that are otherwise nearly impossible to observe experimentally.
One of the project’s key achievements was its ability to make the deeply cooled, metastable state of water—often called “No Man’s Land” due to its experimental inaccessibility—more accessible for study. This state is believed to hold the secrets to water’s unique properties but has been challenging to probe without crystallization occurring. The simulations enabled us to study this elusive state and identify the structural dynamics that contribute to water’s unusual behavior.
The implications of these findings are far-reaching. By deepening our understanding of water, the project contributes to more accurate climate modeling, where water plays a critical role in predicting weather patterns and understanding global changes. Furthermore, the ability to “tune” molecules to mimic water’s behavior could revolutionize industrial processes and lead to innovative applications in colloidal science, where insoluble particles are suspended in a medium.
The SOFTWATER project highlights the power of computational modeling in uncovering fundamental insights into material behavior and opens new avenues for research across disciplines.
The project developed innovative theoretical and computational methodologies to study the anomalies of water. A significant achievement was the demonstration that water’s potential can be systematically tuned to transition continuously between a simple liquid and a tetrahedrally coordinated liquid. To capture and analyze the structural changes associated with this transition, new analytical tools were developed, inspired by topology (persistent homology) and machine learning (neural network descriptors).
These methodological advancements provided critical insights into the anomalous behavior of water in its liquid state. Specifically, we demonstrated that tuning tetrahedral interactions allows for a smooth interpolation between behaviors predicted by different theoretical scenarios of water. Using a two-state water model, we rationalized the behavior observed in simulations, offering both qualitative and quantitative explanations for water’s anomalies.
Beyond the liquid phase, the project significantly advanced our understanding of water’s glassy and crystalline states. For the glassy phase, we established that the glass-forming ability correlates strongly with a single thermodynamic quantity, which we termed the “thermodynamic interface penalty.” This finding extends beyond water, providing a general framework applicable to systems with competing interactions, such as metallic glass formers. For the crystalline phase, we made seminal contributions by elucidating the role of defects in water’s polymorphic behavior and their influence on nucleation pathways.
The project also bridged the study of water with advancements in Soft Matter Physics, applying insights gained from water to develop a deeper understanding of tetrahedrally coordinated colloidal systems. This effort led to the co-development of the SAT-assembly framework, a powerful tool for solving inverse self-assembly problems. Using this framework, we successfully demonstrated the colloidal assembly of open crystal structures.
The dissemination of the project’s results was achieved through multiple channels. Key findings were published in high-impact peer-reviewed journals, ensuring wide visibility within the scientific community. Additionally, the project’s outcomes were presented at international workshops and conferences, facilitating knowledge exchange and fostering collaborations. To extend the impact beyond academia, two patents were filed, highlighting the potential for technological and industrial applications of the methodologies developed. These efforts underscore the project’s commitment to ensuring its findings reach both scientific and practical audiences.
These methodological advancements provided critical insights into the anomalous behavior of water in its liquid state. Specifically, we demonstrated that tuning tetrahedral interactions allows for a smooth interpolation between behaviors predicted by different theoretical scenarios of water. Using a two-state water model, we rationalized the behavior observed in simulations, offering both qualitative and quantitative explanations for water’s anomalies.
Beyond the liquid phase, the project significantly advanced our understanding of water’s glassy and crystalline states. For the glassy phase, we established that the glass-forming ability correlates strongly with a single thermodynamic quantity, which we termed the “thermodynamic interface penalty.” This finding extends beyond water, providing a general framework applicable to systems with competing interactions, such as metallic glass formers. For the crystalline phase, we made seminal contributions by elucidating the role of defects in water’s polymorphic behavior and their influence on nucleation pathways.
The project also bridged the study of water with advancements in Soft Matter Physics, applying insights gained from water to develop a deeper understanding of tetrahedrally coordinated colloidal systems. This effort led to the co-development of the SAT-assembly framework, a powerful tool for solving inverse self-assembly problems. Using this framework, we successfully demonstrated the colloidal assembly of open crystal structures.
The dissemination of the project’s results was achieved through multiple channels. Key findings were published in high-impact peer-reviewed journals, ensuring wide visibility within the scientific community. Additionally, the project’s outcomes were presented at international workshops and conferences, facilitating knowledge exchange and fostering collaborations. To extend the impact beyond academia, two patents were filed, highlighting the potential for technological and industrial applications of the methodologies developed. These efforts underscore the project’s commitment to ensuring its findings reach both scientific and practical audiences.
The SOFTWATER project achieved remarkable progress beyond the state of the art in understanding water and water-like systems, introducing innovative theoretical models, computational techniques, and frameworks that have redefined how we study this fundamental substance.
One of the key breakthroughs was the development of the first theoretical model that integrates both static and dynamic anomalies of water: the hierarchical two-state model. This model provides a unified explanation for water’s unique behavior, attributing its thermodynamic anomalies to the temperature- and pressure-dependent population of locally favored structures and its dynamic anomalies to the interplay between these structures.
The project also introduced the concept of water as a “half-empty liquid”, redefining water as a new class of potential. This mean-field model captures the influence of directional bonding and topological constraints imposed by the tetrahedral geometry, accounting for water’s unique anomalies, the liquid-liquid transition, and its crystallization behavior.
A pioneering achievement of the project was the first application of neural networks to study locally favored structures in water and amorphous ices. This computational approach enabled the identification of structural features that are challenging to detect using conventional methods.
Complementing this was the development of a novel neural network potential for water, incorporating advanced atomic fingerprints that account for both two- and three-body interactions. This potential demonstrated exceptional accuracy in reproducing properties beyond its training set, including dynamic behaviors, thermodynamic anomalies, and the stability of crystalline phases. The neural network potential represents a significant step forward in computational modeling, offering unprecedented precision in capturing water’s complex behavior.
Finally, the project advanced self-assembly science by creating the SAT-assembly framework, a versatile tool for solving the inverse self-assembly problem. This framework enabled the precise design of systems to self-assemble into targeted structures, including the successful crystallization of a colloidal pyrochlore lattice.
Collectively, these breakthroughs represent a significant leap beyond the existing state of the art, providing novel theoretical and computational tools that pave the way for future research into water and related systems. The methodologies developed in the SOFTWATER project have far-reaching implications for condensed matter physics, materials science, and the design of complex self-assembling systems.
One of the key breakthroughs was the development of the first theoretical model that integrates both static and dynamic anomalies of water: the hierarchical two-state model. This model provides a unified explanation for water’s unique behavior, attributing its thermodynamic anomalies to the temperature- and pressure-dependent population of locally favored structures and its dynamic anomalies to the interplay between these structures.
The project also introduced the concept of water as a “half-empty liquid”, redefining water as a new class of potential. This mean-field model captures the influence of directional bonding and topological constraints imposed by the tetrahedral geometry, accounting for water’s unique anomalies, the liquid-liquid transition, and its crystallization behavior.
A pioneering achievement of the project was the first application of neural networks to study locally favored structures in water and amorphous ices. This computational approach enabled the identification of structural features that are challenging to detect using conventional methods.
Complementing this was the development of a novel neural network potential for water, incorporating advanced atomic fingerprints that account for both two- and three-body interactions. This potential demonstrated exceptional accuracy in reproducing properties beyond its training set, including dynamic behaviors, thermodynamic anomalies, and the stability of crystalline phases. The neural network potential represents a significant step forward in computational modeling, offering unprecedented precision in capturing water’s complex behavior.
Finally, the project advanced self-assembly science by creating the SAT-assembly framework, a versatile tool for solving the inverse self-assembly problem. This framework enabled the precise design of systems to self-assemble into targeted structures, including the successful crystallization of a colloidal pyrochlore lattice.
Collectively, these breakthroughs represent a significant leap beyond the existing state of the art, providing novel theoretical and computational tools that pave the way for future research into water and related systems. The methodologies developed in the SOFTWATER project have far-reaching implications for condensed matter physics, materials science, and the design of complex self-assembling systems.