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What is a Quasicrystal? Approaches, And Future Implications

This article explains quasicrystals, their methods, and future implications.

What Creates Quasicrystals? Scientists Present First Quantum-Mechanical Model of Stability

A groundbreaking University of Michigan study that conducted the first quantum-mechanical simulations of quasicrystals found these strange materials to be fundamentally stable. This novel research overcomes classical  quantum mechanics  limits, solving quasicrystal understanding problems. The Nature Physics findings suggest that quasicrystals behave like stable crystals in their atomic arrangements despite their glass-like disorganised appearance.

What is a Quasicrystal

For decades, quasicrystals have puzzled scientists as a strange intermediate form between chaotic amorphous solids like glass and highly organised crystals. In contrast to ordinary crystals, quasicrystals have organised lattices but no infinitely repeating atomic patterns. After discovering a five-fold symmetry aluminium and manganese alloy in 1984, Israeli scientist Daniel Shechtman was the first to describe it.

Previously, this characteristic was supposed to prevent crystal repeating patterns. Shechtman won the 2011 Nobel Prise in Chemistry despite substantial criticism after other laboratories confirmed their existence and found them in billion-year-old meteorites. Quasicrystals lack indefinitely repeating patterns, which density functional theory (DFT), a classic quantum-mechanical technique, requires, hence their stability remains unsolved.

A New Simulation Method:

PhD student Woohyeon Baek and Dow Early Career Assistant Professor of Materials Science and Engineering Wenhao Sun developed a new modelling method to solve this problem. Instead of repeating, they “scoop out” tiny nanoparticles from a larger simulated quasicrystal block. Calculating the overall energy of these limited nanoparticles and extrapolating across increasing sizes can accurately predict the bulk quasicrystal's energy. Baek said, “The first step to understanding a material is knowing what makes it stable, but it has been hard to tell how quasicrystals were stabilised.”

Crystal-Stabilized Enthalpy:

The researchers found that two well-studied quasicrystal alloys of ytterbium-cadmium and scandium-zinc are “enthalpy-stabilized,” like crystals. Crystals are stable because their atomic configurations reduce chemical bond energy, unlike glass, which is “entropy-stabilized” by rapid cooling that freezes atoms into many chaotic arrangements.

Overcoming Computational Challenges:

Simulating the biggest nanoparticles was important to estimate energy accurately, which is difficult due to computing time scaling issues. Computing time may grow eightfold if nanoparticles were multiplied, even to hundreds. The GPU-accelerated method, developed with Professor Vikram Gavini, lowers processor-to-processor communication and speeds processing 100 times. This discovery simplified quasicrystal analysis and allowed simulation of complicated materials including glass, amorphous solids, and quantum computing-relevant crystal flaws.

Discovering Glass-Former Dynamic Similarities:

University of Michigan study showed that quasicrystals are structurally stable and resemble crystals, but a closer look at their dynamic properties revealed unexpected similarities to metallic glass-forming liquids. According to molecular dynamics simulations, glass-forming liquids and heated crystalline solids have quick beta and delayed alpha relaxation. Like quasicrystals and metallic glasses, they have a “kink” on Arrhenius plots for the temperature dependence of their diffusion coefficient and structural relaxation time.

Most crucially, dynamic heterogeneity in particle mobility fluctuations showed that the non-Gaussian parameter's peak value climbs with cooling, a behaviour more reminiscent of glass-forming liquids than hot crystals. Two types of atomic motion were found: isolated “phason flips” at low temperatures and frequent “string-like collective motions” at higher temperatures, which resembled glasses.

The “decoupling phenomenon,” the Stokes-Einstein link disintegration, best illustrates their glass-like dynamics. A fractional Stokes-Einstein relation with a decoupling exponent of 0.33 was found in which the temperature-normalized self-diffusion coefficient scales with structural relaxation time. Glass-forming liquids dissociate, whereas crystalline solids do not. This means metallic glass-forming devices are better at quasicrystal dynamics than crystals.

Future Impact:

A US Department of Energy-funded study sheds light on quasicrystals' fundamental properties. It supports the idea that quasicrystals are hybrid matter by connecting solid structure and motion. Phason flip movements and vibrational properties will be studied in three-dimensional quasicrystal models.

[alishaaishu2000]

Quantum Chemistry

Quantum chemistry is a subfield of chemistry focused on the application of quantum mechanics to chemical systems. It investigates the electronic structure, molecular dynamics, and reaction mechanisms of atoms and molecules using principles like Schrödinger’s equation, wavefunctions, and molecular orbitals. This field plays a critical role in predicting molecular behavior, designing new materials, and understanding fundamental chemical processes at the quantum level.

International Chemistry Scientist Awards

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[marketresearchnews00]

Global Medium-Density Fibreboard (MDF)  Market to Witness Great Growth in Forecasted Period 2021-2028

In 2021, the Global Medium-Density Fibreboard (MDF) Market’s size was valued at USD 24.9 billion and is estimated to reach USD 40.6 billion by 2028 and is expected to be growing at a CAGR of 8.96 % throughout the forecast period. In this report, 2021 has been taken as the base year while 2020 is the historical year. The forecast year for the report is 2028 to approximate the size of the market for Medium-Density Fibreboard (MDF).

Get Sample Report at @ https://iconmarketresearch.com/inquiry/sample/IMR1424

 Influencing players of this market are:

·         DAIKEN CORPORATION

·         Dongwha Group

·         Arauco

·         Kronoplus Limited

·         Duratex

 Growth Mapping

The main aim of the Medium-Density Fibreboard (MDF) report is to give the map of the growth for the Medium-Density Fibreboard (MDF) market that will help in providing the clients with the required information for the formulation strategies to meet their respective business goals. The report also provides deep knowledge of the Medium-Density Fibreboard (MDF) for the previous year as well as for the forecast years and what the CAGR level of the market is going to be. This report is a synopsis of what the market conditions are going to be while also giving information regarding the market’s definition, classifications, applications, and engagements are. This report also aims to analyze the Medium-Density Fibreboard (MDF) market’s developments including the market improvement, market position, and others which are usually done by the prominent players and brands of the manufacturing and construction industry. This report also consists of all the market drivers and restraints which are obtained through SWOT analysis. The report also has the CAGR values of the Medium-Density Fibreboard (MDF) market for the base years 2021, the historic year 2020, and forecast years 2021-2028.

 The Report Provides Insights on The Following Pointers:

·         It gives a forecast analysis of factors that are driving or restraining the development of the Medium-Density Fibreboard (MDF) market

·         The report gives a seven-year forecast value evaluated on the basis of the current market performance of the manufacturing and construction industry.

·         It helps in understanding the main segments of the products and their future.

·         The report gives a deep analysis of changing competition in the market which keeps you ahead of your competitors.

·         The report gives the market definition of the Medium-Density Fibreboard (MDF) market along with the analysis of different factors influencing the market such as drivers, opportunities, and restraints.

 #density #densityFunctionalTheory #Densitymag #densitytraining

[govindhtech]

Coupled Cluster, DFT: Accuracy Cost Paradox In Drug Design

Explore the dilemma of quantum mechanical techniques like Coupled Cluster and DFT: they provide unique molecular insights but are too expensive for current drug research.

Overview: Drug Development Needs Innovation

Over the past 50 years, pharmaceutical drug development has become increasingly expensive and time-consuming, costing billions of dollars. Drug development must be improved to fulfil unmet medical requirements.

Computational approaches are already critical to pharmaceutical development. Quantum mechanical computations, molecular dynamics, and machine learning are examples. One bottleneck is designing and optimising chemicals to attach to a disease-related target protein. Computational methods predict binding affinity, a key drug candidate efficacy indicator.

However, simulating chemical systems with thousands of atoms in a cellular environment at restricted temperatures is computationally intensive. Current methods, such as molecular simulations with classical force fields, often fail to anticipate binding affinity. Quantum mechanical methods like Coupled Cluster (CC) and Density Functional Theory (DFT) describe molecular interactions better, but their high processing cost makes them unsuitable for drug creation. Because slight inaccuracies might produce considerable dosage prediction errors, great precision, ideally within 1.0 kcal/mol of experimental results, is desired.

Promise of Quantum Computers

Due to its quantum mechanical properties, quantum computers are being studied as a technique to model quantum systems. The promise of precise and effective quantum chemical computations is a major rationale for financing the study.

Quantum computers are expected to improve molecular system ground state energy determination. Traditional methods fail in systems with high correlations.

Strong electronic correlation is indicated by multi-reference wavefunctions, essential spin-symmetry breaking, cluster expansion failure sites, and near-degenerate natural orbitals. Multi-metal systems may require expensive multi-reference treatment.

Potential Quantum Computer Uses: Phase Estimation

Quantum Phase Estimation (QPE) is the typical electronic structure computation approach on fault-tolerant quantum computers. Creating the error-corrected quantum circuit, determining an initial quantum state, and fine-tuning the chemical system geometry usually begin on a classical computer.

Quantum computing then prepares this conventionally defined starting state. The ground state energy is then calculated using QPE. The efficiency of QPE depends on how closely the starting state matches the ground state. With adjustments to this approach, molecular forces and other important properties may be calculated.

Important Challenges Remain

There are many barriers to using quantum computers for large-scale drug discovery, notwithstanding their theoretical benefits.

Limits on Technology:

Noisy Intermediate Scale Quantum (NISQ) technology, with few qubits and noise, is now in use. To gain a quantum advantage for complex chemical calculations, Fault-Tolerant Quantum Computers (FTQCs) must exponentially minimise mistakes. FTQCs are a major engineering difficulty.

Even the iron-molybdenum complex (FeMoco), a difficult chemical, would require 200 logical qubits and millions of physical qubits after error correction. This is much larger than existing hardware can handle. Quantum error correction is a major run-time and qubit count overhead. To lower these overheads, quantum error correction codes and algorithms, hardware with lower error rates, and qubit connection must improve.

Problems with algorithms

Major algorithmic issues remain. Effectively preparing the initial quantum state is difficult. Despite heuristic approaches, more research is needed because the overlap of this starting state with the planned ground state directly influences QPE runtime. Another necessity for minimising processing cost is finding more compact Hamiltonian representations.

Drug design challenges include calculating thermodynamic characteristics like binding affinity, which may be the most significant impediment. Obtaining ensemble properties may involve billions of single-point calculations. Even if quantum computers could speed up individual computations, the sheer volume of calculations necessary makes it impossible to generate conclusions in a timely manner compared to well optimised experiments (current run-time estimates for sophisticated systems are days).

Adding an explicit solvent like water increases computing complexity and requirements. Drug design requires efficient computing of thermodynamic parameters, even when single-point simulations yield insights. Two methods are directly modelling electrons and classical nuclei or building thermal ensembles of geometries on a quantum computer.

Potential Effects and Use Cases

Quantum computing may have other uses in drug development, but its largest impact is predicted to be on lead optimisation computations. These comprise NMR and IR molecular spectra for structure identification and drug manufacturing reaction process refinement. Compared to speeding up core lead optimisation, these regions are expected to have less influence.

Quantum computers are best for precise computations on densely linked systems that classical methods cannot handle. Advanced approaches like DFT and Coupled Cluster would likely have the greatest impact on the pharmaceutical industry, even if used less accurately. Quantum computers may offer new ways to improve classical approaches, such as DFT functionals, even if they cannot speed linear-scaling classical methods like DFT or Hartree-Fock. Coupled Cluster approaches on quantum computers could triple optimisation speed.

Conclusion

The limits of quantum chemistry for drug development are either the high cost of DFT calculations for large biomolecular ensembles or the lack of accuracy for complex systems. Quantum computers may solve the accuracy problem for strongly correlated systems, but ensemble calculations to derive thermodynamic parameters are still expensive.

Quantum methods for electronic structure problems have reduced computational costs in recent decades. In addition to hardware breakthroughs and error correction codes (such as state preparation), more algorithmic advances are needed to go beyond single-point energy calculations and impact the pharmaceutical industry.

Despite the challenges, open research between academia and business may yield the fundamental advances needed to make quantum computing a critical tool for generating better drugs faster. Some of these issues are addressed. For computational drug design to be truly predictive and more broadly applicable, quantum computers must deliver the accuracy and robustness for strongly and weakly correlated systems at rates comparable to lower-precision conventional approaches.

One ambitious future goal that will require massive quantum computers is applying quantum machine learning to quantum calculations to predict pharmacokinetics.