Periodic Reporting for period 1 - EIM (Towards Elemental Identification in Single Molecules using Atomic Force Microscopy)
Période du rapport: 2021-01-01 au 2022-12-31
The project has achieved the following major scientific results:
a. Realizing a combination of atomic force microscopy imaging and machine learning infrastructure that allow direct visualization of the electrostatic field of a single molecule
1. What is the problem/issue being addressed?
There has been a lack of effective techniques for directly characterizing the electrostatic properties of molecules, particularly at the nanoscale. While various methods, such as scanning probe microscopy and functionalized tips in atomic force microscopy (AFM), can provide indirect information on electrostatic properties, there is a need for more systematic techniques that can accurately and directly measure these properties. Kelvin probe microscopy (KPFM) has been introduced as a way to explore the topography and local contact potential difference of surfaces and proteins with atomic resolution. Still, it has limitations such as longer measurement times, the need for functionalized tips, and difficulty obtaining quantitative measurements. Other techniques, such as scanning quantum dot microscopy, have also been developed, but they rely on specialized equipment and are not widely implemented.
2. Why is it important for society?
The problem of effectively characterizing the electrostatic properties of molecules is important for society because these properties play a crucial role in a wide range of technologies and processes. For example, understanding the electrostatic properties of molecules can help in designing and controlling functionality at the nanoscale, which has applications in fields such as catalysis, molecular electronics, and biological functions. Being able to accurately and directly measure electrostatic properties can also help in understanding the catalytic activity of molecules, identifying products of on-surface synthesis reactions, and facilitating the chemical identification of molecules. These capabilities can have significant impacts in areas such as materials science, chemical synthesis, and biomedical research.
3. What are the overall objectives?
The overall objectives are to develop a systematic technique for directly characterizing the electrostatic properties of molecules, particularly at the nanoscale, and to overcome the limitations of existing methods such as Kelvin probe microscopy (KPFM). We propose a machine learning approach called electrostatic discovery atomic force microscopy (ED-AFM) that can predict accurate electrostatic fields from standard atomic force microscopy (AFM) images as a convenient and accurate way to study the electrostatic potential of molecules adsorbed on surfaces. This approach can be used to understand the catalytic activity of molecules, identify products of on-surface synthesis reactions, and facilitate the chemical identification of molecules, among other applications.
b. Resolving the detailed structure of water dimer and how they affect DNA base supramolecular assemblies
1. What is the problem/issue being addressed?
Water dimers, which are pairs of water molecules that are bound together, are important for understanding the properties of water. They have been shown to have some unusual properties, such as a low barrier for movement on a surface due to nuclear quantum effects. Water dimers can also act as catalysts and are involved in the formation of ice nanoclusters. However, it is difficult to isolate and study individual water dimers at room temperature. Additionally, many experimental techniques have limited resolution, provide average results for a group of molecules rather than information about individual molecules, or require complex modeling to analyze the data.
2. Why is it important for society?
It is important for studying the properties of water and has potential applications as catalysts and in the formation of ice nanoclusters. Understanding water dimers and their role in biochemical processes and self-assembly could have significant implications for a variety of fields, including chemistry and biology.
3. What are the overall objectives?
The overall objectives are, (a) to study the properties of water dimers and their potential roles in chemical reactions and functional materials, (b) to improve understanding of water dimers by obtaining high-resolution structural data at the single-molecule level, (c) to use molecular assemblies to directly study the properties of water dimers, (d) to use non-covalent interactions and hydrogen bonding to gain insights into micro-hydration and the role of confined water in DNA bases, and (e) to explore the potential role of individual water dimers in self-assembly and the novel properties and future applications of water dimers.
a. Realizing a combination of atomic force microscopy imaging and machine learning infrastructure that allow direct visualization of the electrostatic field of a single molecule
1. What is the problem/issue being addressed?
There has been a lack of effective techniques for directly characterizing the electrostatic properties of molecules, particularly at the nanoscale. While various methods, such as scanning probe microscopy and functionalized tips in atomic force microscopy (AFM), can provide indirect information on electrostatic properties, there is a need for more systematic techniques that can accurately and directly measure these properties. Kelvin probe microscopy (KPFM) has been introduced as a way to explore the topography and local contact potential difference of surfaces and proteins with atomic resolution. Still, it has limitations such as longer measurement times, the need for functionalized tips, and difficulty obtaining quantitative measurements. Other techniques, such as scanning quantum dot microscopy, have also been developed, but they rely on specialized equipment and are not widely implemented.
2. Why is it important for society?
The problem of effectively characterizing the electrostatic properties of molecules is important for society because these properties play a crucial role in a wide range of technologies and processes. For example, understanding the electrostatic properties of molecules can help in designing and controlling functionality at the nanoscale, which has applications in fields such as catalysis, molecular electronics, and biological functions. Being able to accurately and directly measure electrostatic properties can also help in understanding the catalytic activity of molecules, identifying products of on-surface synthesis reactions, and facilitating the chemical identification of molecules. These capabilities can have significant impacts in areas such as materials science, chemical synthesis, and biomedical research.
3. What are the overall objectives?
The overall objectives are to develop a systematic technique for directly characterizing the electrostatic properties of molecules, particularly at the nanoscale, and to overcome the limitations of existing methods such as Kelvin probe microscopy (KPFM). We propose a machine learning approach called electrostatic discovery atomic force microscopy (ED-AFM) that can predict accurate electrostatic fields from standard atomic force microscopy (AFM) images as a convenient and accurate way to study the electrostatic potential of molecules adsorbed on surfaces. This approach can be used to understand the catalytic activity of molecules, identify products of on-surface synthesis reactions, and facilitate the chemical identification of molecules, among other applications.
b. Resolving the detailed structure of water dimer and how they affect DNA base supramolecular assemblies
1. What is the problem/issue being addressed?
Water dimers, which are pairs of water molecules that are bound together, are important for understanding the properties of water. They have been shown to have some unusual properties, such as a low barrier for movement on a surface due to nuclear quantum effects. Water dimers can also act as catalysts and are involved in the formation of ice nanoclusters. However, it is difficult to isolate and study individual water dimers at room temperature. Additionally, many experimental techniques have limited resolution, provide average results for a group of molecules rather than information about individual molecules, or require complex modeling to analyze the data.
2. Why is it important for society?
It is important for studying the properties of water and has potential applications as catalysts and in the formation of ice nanoclusters. Understanding water dimers and their role in biochemical processes and self-assembly could have significant implications for a variety of fields, including chemistry and biology.
3. What are the overall objectives?
The overall objectives are, (a) to study the properties of water dimers and their potential roles in chemical reactions and functional materials, (b) to improve understanding of water dimers by obtaining high-resolution structural data at the single-molecule level, (c) to use molecular assemblies to directly study the properties of water dimers, (d) to use non-covalent interactions and hydrogen bonding to gain insights into micro-hydration and the role of confined water in DNA bases, and (e) to explore the potential role of individual water dimers in self-assembly and the novel properties and future applications of water dimers.
a. Electrostatic discovery atomic force microscopy
We present ED-AFM, a machine learning (ML) method that can predict precise electrostatic fields from a set of standard AFM images. In this work, a neural network takes two sets of microscope images as input and translates them to the electrostatic map of the molecule. The neural network is trained on simulated sets of input-output pairs calculated from a database of several tens of thousands of molecule geometries. The trained model was then applied to experimental mages to predict the molecular electric field. This technique can help us study the electrostatic potential of molecules on surfaces, which is relevant for understanding their catalytic activity, identifying products of on-surface synthesis routes, and facilitating chemical identification of functional groups in unknown molecules.
b. Water Dimer-Driven DNA Base Superstructure with Mismatched Hydrogen Bonding
With STM, AFM, and DFT simulations, we successfully revealed the detailed bonding structure of the confined water dimers as well as the re-arrangement the local adenine network undergoes upon hydration. The water dimers under confinement appear to be in a linear non-planar configuration, causing a local chirality inversion such that a distinctive mismatched H-bond pattern emerged between neighboring adenine molecules. The comprehensive characterization of the ensemble of water dimers and adenine not only provides crucial insights into the dynamic nature of the hydration process of DNA bases but also offers a novel method to study unstable small-molecule clusters which would otherwise be impossible to observe.
We present ED-AFM, a machine learning (ML) method that can predict precise electrostatic fields from a set of standard AFM images. In this work, a neural network takes two sets of microscope images as input and translates them to the electrostatic map of the molecule. The neural network is trained on simulated sets of input-output pairs calculated from a database of several tens of thousands of molecule geometries. The trained model was then applied to experimental mages to predict the molecular electric field. This technique can help us study the electrostatic potential of molecules on surfaces, which is relevant for understanding their catalytic activity, identifying products of on-surface synthesis routes, and facilitating chemical identification of functional groups in unknown molecules.
b. Water Dimer-Driven DNA Base Superstructure with Mismatched Hydrogen Bonding
With STM, AFM, and DFT simulations, we successfully revealed the detailed bonding structure of the confined water dimers as well as the re-arrangement the local adenine network undergoes upon hydration. The water dimers under confinement appear to be in a linear non-planar configuration, causing a local chirality inversion such that a distinctive mismatched H-bond pattern emerged between neighboring adenine molecules. The comprehensive characterization of the ensemble of water dimers and adenine not only provides crucial insights into the dynamic nature of the hydration process of DNA bases but also offers a novel method to study unstable small-molecule clusters which would otherwise be impossible to observe.
a. Besides single molecules, ED-AFM can be used to make predictions for assembled layers, defects, and charge dynamics. The method can also be applied to images of systems where direct simulation is impossible due to size, complexity, or lack of information. Combined with developments in the autonomous functionalization of the tip in SPM, this promises a future of potential in electrostatics for ED-AFM.
b. Our comprehensive characterization of the ensemble of water dimers and adenine not only provides crucial insights into the dynamic nature of the hydration process of DNA bases but also offers a novel method to study unstable small-molecule clusters which would otherwise be impossible to observe.
b. Our comprehensive characterization of the ensemble of water dimers and adenine not only provides crucial insights into the dynamic nature of the hydration process of DNA bases but also offers a novel method to study unstable small-molecule clusters which would otherwise be impossible to observe.