How many times Machine learning can accelerate excited state dynamics?5 answersMachine learning can accelerate excited-state dynamics multiple times across various studies. For instance, the introduction of a neural network potential called the Diabatic Artificial Neural Network (DANN) has shown to be six orders of magnitude faster than traditional quantum chemistry methods. Additionally, the implementation of multi-layer perceptrons in predicting excited state energies has significantly reduced the computational time required for calculations, making it faster than corresponding quantum mechanics/molecular mechanics (QM/MM) methods. Furthermore, the development of Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD) software has enabled simulations that are orders of magnitude faster than traditional methods, allowing for simulations of up to 10 ns in just 2 days. These advancements collectively demonstrate the substantial acceleration of excited-state dynamics through machine learning techniques.
What is the core-shell structure in material?4 answersThe core-shell structure in materials refers to a design where there is a core material surrounded by a shell material. This structure allows for the combination of different functionalities and properties from the core and shell components. Core-shell nanoparticles have gained significant attention in various fields due to their specific design and geometry. They can be classified into different types based on the number of cores and shells present. The synthesis of core-shell nanostructures involves processes such as deposition and heat preservation. These nanostructures have found applications in areas such as drug delivery, bioimaging, and solar cell technology. The core-shell structure provides enhanced thermal stability, solubility, and lower toxicity compared to other materials.
The caldesmon switching the tropomyosin to on state?3 answersCaldesmon switches the thin filament to the OFF state. Caldesmon inhibits actin activation of myosin ATPase activity by displacing S1.ADP.Pi from actin. Caldesmon inhibits actin-tropomyosin activation without displacing S1.ADP.Pi, indicating that it inhibits a rate-limiting step. Caldesmon binding switches actin-tropomyosin to the "off" or "weak" state, and Ca2+/calmodulin binding to caldesmon blocks this switch and reactivates the actin filament. Caldesmon can switch actin-tropomyosin from motile to non-motile states without controlling velocity of movement or weak binding affinity.
What are the commonly used computational methods to study excited states of transition metal clusters?5 answersCommonly used computational methods to study excited states of transition metal clusters include the systematic cluster growth (SCG) method combined with density functional theory (DFT) calculations. This method allows for the investigation of the structural evolution and growth pattern of transition metal clusters, and has been successful in finding the ground state structures and growth patterns for various transition metal clusters. Additionally, variational density functional theory approaches have been developed to study excited electronic states, overcoming limitations of the self-consistent field (SCF) procedure. Coupled-cluster theory combined with perturbation theory has also been used to evaluate the performance of computational approaches for treating excitations in transition metal clusters. These methods provide valuable insights into the photophysics and excited state properties of transition metal systems, which are important for the development of materials with applications in energy conversion and molecular devices.
What are the best computational methods to study excited states of transition metal clusters?5 answersThe best computational methods to study excited states of transition metal clusters include high-order coupled-cluster (CC) calculations with diffuse containing basis sets, which provide highly accurate excited-state properties. Another approach is the combination of long-range corrected tight-binding density-functional fragment molecular orbital method (FMO-LC-DFTB) with an excitonic Hamiltonian, which allows for efficient calculation of electronically excited states in large molecular assemblies. Variational density functional theory approaches to excited electronic states can also be used, although limitations of the commonly used self-consistent field (SCF) procedure need to be addressed. Additionally, approaches that combine coupled-cluster and perturbation theory based on a predefined active space of orbitals have shown good performance in treating excitations in transition metal clusters. These computational methods provide valuable insights into the electronic structure and reactivity of transition metal clusters, which are important for understanding their catalytic properties.
As a researcher Why would someone think the study of 'states of matter' is important?5 answersThe study of 'states of matter' is important for several reasons. Understanding the fundamental ideas and particles that make up the universe helps us comprehend the behavior of matter and how it is put together. It allows us to explain why liquids take the shape of their container while solids retain their own shape. Additionally, exploring the different physical states of matter, such as liquids, solids, and gases, provides insights into density, cohesion, and the properties of materials. The study of matter also led to the development of thermodynamics and microscopic interpretations, which have greatly contributed to our understanding of the natural sciences. Overall, investigating the states of matter enables us to unravel the complexities of the physical world and make connections between the macroscopic and microscopic levels of existence.