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Reactive Force Fields
Reactive Force Fields
Molecular dynamics (MD) is a widely used approach for studying the properties of molecules and materials at the atomistic level. Long-timescale MD simulations are essential for advancing various scientific fields, including chemistry, biology, and materials science. Reactive force fields (ReaxFF) have enabled the modeling of bond formation and backing in complex materials with hundreds of thousands of explicitly treated atoms in these scientific fields. This powerful computational tool has been successfully used to model various aqueous chemical systems, including amino acid protonation in water and weak interactions in pure water systems, and for the optimization and development of new biomaterials, polymers, and batteries, particularly because quantum mechanical simulations on large-scale systems are much more computationally intensive, if not impossible. However, unlike quantum methods, ReaxFF calculations depend on system-specific and non-transferable interatomic parameters, such as bond-order force fields, which must be derived by fitting quantum mechanical results or experimental data. Furthermore, while ReaxFF simulations allow the study of very large systems with quantum mechanical accuracy at a fraction of the cost, they remain computationally intensive. Approximately 80% of this computational cost is due to the iterative solving of linear equations to determine the equilibrium value of electronegativity across the entire system and the partial charges on atoms. This high computational cost limits the timescale of reactive simulations to much shorter durations compared to experimental timeframes. Non-transferable parameters and high computational cost are also the primary reasons why, despite ReaxFF’s potential for simulating biological processes, it has not yet been widely adopted in biological mechanistic studies.
Molecular dynamics (MD) is a widely used approach for studying the properties of molecules and materials at the atomistic level. Long-timescale MD simulations are essential for advancing various scientific fields, including chemistry, biology, and materials science. Reactive force fields (ReaxFF) have enabled the modeling of bond formation and backing in complex materials with hundreds of thousands of explicitly treated atoms in these scientific fields. This powerful computational tool has been successfully used to model various aqueous chemical systems, including amino acid protonation in water and weak interactions in pure water systems, and for the optimization and development of new biomaterials, polymers, and batteries, particularly because quantum mechanical simulations on large-scale systems are much more computationally intensive, if not impossible. However, unlike quantum methods, ReaxFF calculations depend on system-specific and non-transferable interatomic parameters, such as bond-order force fields, which must be derived by fitting quantum mechanical results or experimental data. Furthermore, while ReaxFF simulations allow the study of very large systems with quantum mechanical accuracy at a fraction of the cost, they remain computationally intensive. Approximately 80% of this computational cost is due to the iterative solving of linear equations to determine the equilibrium value of electronegativity across the entire system and the partial charges on atoms. This high computational cost limits the timescale of reactive simulations to much shorter durations compared to experimental timeframes. Non-transferable parameters and high computational cost are also the primary reasons why, despite ReaxFF’s potential for simulating biological processes, it has not yet been widely adopted in biological mechanistic studies.
To address these challenges, we introducing transferable interatomic parameters into the ReaxFF framework and accelerating its simulations by reducing iterative calculations to a single-step process. These revisions are based on our recently developed atomic model which was used for deriving the Morse potential and serves as the foundation for proposed reformulations in the ReaxFF framework. This research accelerates reactive molecular dynamics simulations and enable longer timescale ReaxFF simulations without compromising their high accuracy.
To address these challenges, we introducing transferable interatomic parameters into the ReaxFF framework and accelerating its simulations by reducing iterative calculations to a single-step process. These revisions are based on our recently developed atomic model which was used for deriving the Morse potential and serves as the foundation for proposed reformulations in the ReaxFF framework. This research accelerates reactive molecular dynamics simulations and enable longer timescale ReaxFF simulations without compromising their high accuracy.
Photoswitches for Photopharmacology and Smart Materials
Photoswitches for Photopharmacology and Smart Materials
A thermally reversible photochromic system, where the photogenerated isomer is thermally unstable and reverts to the initial isomer via thermal relaxation, is known as a T-type photoswitch (figure) and serves as the foundation for a wide range of applications. Photopharmacology, an emerging field in medicine, utilizes photochromic systems to address the long-lasting issue of poor selectivity in drug delivery by linking photochemistry to the pharmacology of drug molecules. The successful proof-of-concept for high-precision and non-invasive optical control of pharmacological activity with minimal side effects in this interdisciplinary field is heavily dependent on the efficiency of the photoswitch molecules used. However, most of the currently available photoswitch molecules are only active in the highly energetic UV range, which significantly hinders the growth of photopharmacology. To overcome this challenge, the development of red-light switches is critical, as this part of spectrum is more penetrant and less damaging compared to UV light. However, the reduced energy gap between ground and excited electronic states in red shifting exponentially decreases the photoisomerization yield (quantum yield in the figure) due to energy loss to non-reactive motions.
A thermally reversible photochromic system, where the photogenerated isomer is thermally unstable and reverts to the initial isomer via thermal relaxation, is known as a T-type photoswitch (figure) and serves as the foundation for a wide range of applications. Photopharmacology, an emerging field in medicine, utilizes photochromic systems to address the long-lasting issue of poor selectivity in drug delivery by linking photochemistry to the pharmacology of drug molecules. The successful proof-of-concept for high-precision and non-invasive optical control of pharmacological activity with minimal side effects in this interdisciplinary field is heavily dependent on the efficiency of the photoswitch molecules used. However, most of the currently available photoswitch molecules are only active in the highly energetic UV range, which significantly hinders the growth of photopharmacology. To overcome this challenge, the development of red-light switches is critical, as this part of spectrum is more penetrant and less damaging compared to UV light. However, the reduced energy gap between ground and excited electronic states in red shifting exponentially decreases the photoisomerization yield (quantum yield in the figure) due to energy loss to non-reactive motions.
Our research objective is to establish principles that facilitate the design of efficient molecular photowsitches active in the visible to infrared spectrum of the light for photopharmacuticals. Specifically, we investigate parameters that minimize non-reactive molecular motions, thereby maximizing quantum yield in red-shifted photoswitches, and the influence of these tunings on the thermal relaxation rate.
Our research objective is to establish principles that facilitate the design of efficient molecular photowsitches active in the visible to infrared spectrum of the light for photopharmacuticals. Specifically, we investigate parameters that minimize non-reactive molecular motions, thereby maximizing quantum yield in red-shifted photoswitches, and the influence of these tunings on the thermal relaxation rate.
Computational Tool for Product Ratio Control
Computational Tool for Product Ratio Control
Among quantum methods developed to systematically explore the reaction potential energy surface (PES), dynamic methods are comprehensive but computationally expensive, while popular static methods are less expensive but limited to minimum energy pathway (MEP) based on classical reaction rate theories. Consequently, the frequently-used static methods are inadequate for predicting and controlling selectivity in reactions where a single transition state leads to more than one product (Figure). For these types of reactions observed in widely used pericyclic and organometallic synthesis strategies, developing new tools based on static methods is crucial, as robust dynamic techniques are computationally demanding. On the other hand, currently available tools are not designed for distribution control and are limited to predicting only the major product within a narrow range of the distribution. The main factor complicating the prediction is the complex shape of PES which stems from the dynamic behavior of atoms and the geometry of the reactants.
Among quantum methods developed to systematically explore the reaction potential energy surface (PES), dynamic methods are comprehensive but computationally expensive, while popular static methods are less expensive but limited to minimum energy pathway (MEP) based on classical reaction rate theories. Consequently, the frequently-used static methods are inadequate for predicting and controlling selectivity in reactions where a single transition state leads to more than one product (Figure). For these types of reactions observed in widely used pericyclic and organometallic synthesis strategies, developing new tools based on static methods is crucial, as robust dynamic techniques are computationally demanding. On the other hand, currently available tools are not designed for distribution control and are limited to predicting only the major product within a narrow range of the distribution. The main factor complicating the prediction is the complex shape of PES which stems from the dynamic behavior of atoms and the geometry of the reactants.
Given the high computational cost of dynamic approaches, even for this relatively simple system, our research seeks to develop a tool that enables martials scientists to predict and control product distribution using bond-order analysis. This research offers several advantages, including improved reaction design techniques including organometallic and natural product syntheses, which particularly benefit from PTSB mechanisms.
Given the high computational cost of dynamic approaches, even for this relatively simple system, our research seeks to develop a tool that enables martials scientists to predict and control product distribution using bond-order analysis. This research offers several advantages, including improved reaction design techniques including organometallic and natural product syntheses, which particularly benefit from PTSB mechanisms.
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