Articles tagged with Computational Chemistry
Researchers developed a machine-learning system that predicts how molecules form, cutting lab work time from months to days and reducing costs. The system uses asymmetric cross-coupling reactions to build complex compounds and can be applied across fields, deepening our understanding of chemistry.
A team of researchers at The University of Osaka has found a novel method for creating diastereomers, which are structurally identical molecules with different biological activities. Their approach uses a group-14 allylatrane to control the reaction, resulting in the high-yield synthesis of complex molecules.
A team of researchers at VCU Massey Comprehensive Cancer Center has discovered an innovative way to use a drug already approved in treating irregular heartbeat to selectively target specific functions of enzymes in lymphoma, effectively killing cancer cells and reducing tumor growth with little to no toxicity. The study found that RBF4...
Researchers discovered that a significant drop in calcium levels in the ocean led to a massive decrease in carbon dioxide, driving global cooling and ending the planet's greenhouse era. The study suggests that changes in seawater chemistry played a key role in shaping climate history.
The Nanalysis Edition of KnowItAll combines Wiley's analytical software platform with Nanalysis' specialized NMR database, streamlining spectral interpretation workflows for users. The tailored solution provides immediate access to reference spectra optimized for benchtop NMR instruments, expanding compound identification coverage.
The Organometallic Chemistry and Homogeneous Catalysis group develops multisensitive catalysts with switchable properties using naphthalene diimides and N-heterocyclic carbenes. This simplifies catalyst optimisation, enabling a single catalyst to facilitate various chemical transformations.
Researchers developed MatAgent, an AI framework that leverages a large language model to design new inorganic materials. The system uses natural language reasoning and explains its decisions in plain language, making the design process more efficient and transparent.
A University of Houston chemist has received a nearly $2M grant to develop molecular blueprints for controlling how molecules change shape and reactivity upon absorbing light. This research could lead to breakthroughs in storing and using chemical energy, as well as designing materials that change when exposed to light.
A study by ICIQ and Ben-Gurion University reveals that deuterium source choice can steer hydrogen isotope exchange reactions along different mechanistic routes. The research combines detailed experiments with data science to uncover the importance of evaluating multiple deuterium sources.
McMaster and Pittsburgh researchers have developed a soft material that can perform a NAND logic operation using only three beams of visible light. The breakthrough paves the way for autonomous systems with computation capabilities without traditional electronics.
The 'Otus' supercomputer provides a solution to pressing challenges through its massive parallel computing capacity, allowing researchers to simulate complex processes, identify patterns, and make predictions about future developments. The system also promotes sustainability with indirect free cooling and renewable energy sources.
Researchers developed a machine learning-based workflow, SPaDe-CSP, to predict crystal structures of organic molecules. The workflow narrows the search space by predicting probable space groups and crystal densities before computationally intensive relaxation steps.
Researchers from Hokkaido University developed a computational method to predict the optimal ligand for generating reactive alkyl ketone radicals. The Virtual Ligand-Assisted Screening (VLAS) method successfully identified L4 as the optimal ligand, enabling the generation of ketyl radicals with high yield.
Researchers found that AI models predict protein structures despite modifications in amino acid sequences or ligands, indicating a lack of understanding of physical chemistry. The models only recognize patterns they've seen before and struggle with unknown proteins.
Engineers at the University of Pittsburgh have created a soft material with a nerve net that mimics how simple living systems coordinate motion. The material responds to chemical reactions, producing mechanical movement without electronics or motors.
Abel's projects aim to create locally specialized environments for enzymes, reducing energy consumption and increasing production efficiency in biomanufacturing processes. His lab will also develop modular metabolic reaction networks, combining components to efficiently build new biomanufacturing processes.
Researchers at CARS create detailed maps of chemical reactivity, discovering regions of unexpected outcomes and reconstructing intricate reaction networks. This new understanding enables control over the formation of different major products from a set of starting materials.
University of Michigan researchers have made significant progress in developing a more accurate simulation approach for density functional theory, a widely used method in fundamental chemistry and materials science studies. The new approach has improved the calculation of exchange-correlation functionals, which describe how electrons i...
Researchers developed a new tool that combines electronic structure theories and machine learning to simulate transition metal catalytic dynamics. The Weighted Active Space Protocol (WASP) delivers dramatic speedups, enabling simulations of catalysts under realistic conditions.
Researchers used AI to analyze protein complexes, discovering catch-bonds 'switch on' almost immediately after force is applied. This finding has implications for understanding bacterial attachment, tissue resilience and developing new biomaterials.
Bacteria breathe deep underground without oxygen using nanowires to dispose of excess electrons. Yale scientists found that electrons move rapidly through the wires via a wave-like behavior rather than hopping, defying classical Newtonian laws. This discovery has significant implications for quantum sensing and computation.
A team of scientists observed the earliest steps of ultrafast charge transfer in a complex dye molecule, with high-frequency vibrations playing a central role. The experiments showed that these vibrations initiate charge transport, while processes in the surrounding solvent begin only at a later stage.
Scientists have created the highest-performing underwater adhesive hydrogel technology, exceeding 1 MPa in adhesive strength, using data mining and machine learning. The gels can withstand repeated ocean tides and wave impacts, making them suitable for biomedical engineering and deep-sea exploration applications.
Researchers at Hokkaido University developed a new recipe for muscle-like gel polymers, which exhibit rapid self-strengthening due to mechanophores with weak bonds. The innovative design enables the creation of thermostable materials with improved stability and potential applications in biological fields.
Researchers at OIST have synthesized a stable 20-electron ferrocene derivative, defying the traditional 18-electron rule. This breakthrough could lead to new applications in energy storage, chemical manufacturing, and sustainable chemistry.
Researchers developed a facile hydrogen atom transfer method using xanthone as a promising ketone photocatalyst for activating carboxylic acids. The method generates minimal reaction waste and has broad applicability with over 40 examples. It also reveals a novel photocatalysis of ketones.
A USC-developed shipboard system using limestone and seawater can remove up to half of carbon dioxide emitted from shipping vessels, cutting maritime CO2 emissions by 50%. The process mimics a natural chemical reaction in the ocean, where CO2 is absorbed into water pumped onboard and then neutralized through a bed of limestone.
Researchers developed a novel quantum-centric supercomputing method to calculate electronic energy levels of complex molecules. This breakthrough enables faster and more accurate simulations, paving the way for advancements in fields like materials science, nanotechnology, and drug discovery.
Researchers developed a machine learning framework that can predict how materials respond to electric fields up to a million atoms, accelerating simulations beyond quantum mechanical methods. This allows for accurate, large-scale simulations of material responses to various external stimuli.
A study combines DFT and machine learning to analyze a wide range of epoxides in CO₂ cycloaddition, identifying key molecular descriptors and predicting reactivity trends. The research aims to develop predictive catalyst and substrate design for optimized CO₂ fixation, contributing to greener chemical processes.
Scientists create two innovative electrolysis systems that use urea found in urine and wastewater to produce green hydrogen at a lower cost than traditional methods. The breakthrough overcomes limitations such as toxic by-products and corrosion issues, paving the way for scalable production.
Kyushu University researchers have successfully recreated the fluid dynamics of flowing biological cells using numerical simulations. The study reveals that capsule position depends on deformation and pulsation frequency, enabling precise cell manipulation in research and potential applications in artificial heart development.
A new web platform, AutoSolvateWeb, developed at Emory University enables chemists of all levels to configure and execute complex quantum mechanical simulations through chatting. The free platform uses cloud infrastructure and automates software processes on the backend.
Researchers warn of misunderstandings in handling AI models, highlighting conditions for confidence in predictions. Explainability methods are crucial to understand algorithmic decisions, but interpreting results requires caution due to AI limitations.
Researchers have developed a new sensor to detect hazardous gas leaks in lithium-ion batteries, which could prevent catastrophic failures and enhance the reliability of battery-powered technologies. The sensor detects trace amounts of ethylene carbonate vapour, targeting potential battery failures before they escalate into disasters.
A collaboration between Japanese, Korean, and American researchers found that larger cations suppress platinum dissolution compared to smaller cations. The study reveals a 'cation effect' influencing electrode durability.
The study identifies a new area where a correction for the self-interaction error breaks down, allowing researchers to pinpoint flaws and develop solutions. By refining DFT, scientists can design better catalysts, leading to improvements in fields such as food production and technology.
Insilico Medicine has discovered a potent and selective CDK12/13 dual inhibitor, compound 12b, using AI-powered platforms. The compound shows significant efficacy in breast cancer and AML models without inducing side effects, paving the way for further therapeutic development.
Researchers created new proteins using AI that bind to and neutralize deadly snake toxins, providing a safer alternative to traditional antivenoms. The study's results show an 80-100% survival rate in mice, offering potential benefits for people in developing countries.
Researchers have made a breakthrough in decoding the growth process of Hexagonal Boron Nitride (hBN), a 2D material with unique versatility. The findings reveal the formation of nanoporous hBN, expanding its potential environmental applications, including sensing and filtering pollutants.
A new study uses machine learning to reduce time needed for calculating screening parameters in Koopmans functionals, enabling faster predictions of material spectral properties. Researchers trained a simple model using modest data and achieved accurate results, paving the way for studying temperature-dependent spectral properties.
Researchers at Chiba University have created an electronically controllable sliding molecular machine using a newly modified ferrocene molecule. The discovery overcomes the challenge of stabilizing the fragile ferrocene molecule on a flat surface, enabling precise control of its motion through electrical signals.
Researchers at Colorado State University have developed a new method to break down PFAS, a group of human-made 'forever' chemicals. The system uses an LED light-based photocatalytic approach that can be used at room temperature, offering a more sustainable and efficient solution than traditional chemical manufacturing processes.
Chris Van de Walle, a distinguished professor at UCSB, has been awarded the American Physical Society's 2025 Aneesur Rahman Prize for Computational Physics. He was recognized for his development and application of first-principles methods to compute structural, electronic, and optoelectronic properties of point defects and interfaces.
Researchers at Wayne State University are developing new AI-powered methods to design and develop new drugs, including carbohydrate-based treatments for cancer. The study aims to improve the accuracy of simulations and machine learning models to predict the behavior of complex biological molecules.
Computational insights from ICIQ researchers apply Marcus theory to estimate free-energy barriers in energy transfer processes, enabling the prediction of EnT barriers and facilitating advances in photocatalysis. The asymmetric variant of the Marcus theory provides more accurate barriers for sensitization of alkenes.
A team of scientists successfully interfaced AiiDA with the Alps supercomputer, completing over 100,000 calculations in just 16 hours. The run demonstrated the maturity of Swiss-made software tools for computational materials science and showcased the power of Switzerland's main supercomputing facility.
A study led by Cesar de la Fuente and his team has uncovered sequences for infection-fighting molecules in the genomic data of extinct species. These ancient β-defensins may lead to the creation of new antimicrobial therapies, including antibacterial, antifungal, and antiviral treatments.
A new structure of light has been discovered that can accurately measure chirality in molecules, a property of asymmetry important in physics, chemistry, biology, and medicine. This 'chiral vortex' provides an accurate and robust form of measurement, allowing for the detection of chiral biomarkers.
Researchers at the University of Illinois have developed a method to understand and improve light-harvesting molecules for solar energy applications. By combining AI with automated chemical synthesis and experimental validation, they were able to produce molecules four times more stable than traditional ones.
A research team led by Magnus Wolf-Watz at Umeå University has discovered the chemistry behind the cell's energy molecule ATP. They found that a small angle change caused by magnesium can significantly speed up the chemical reaction producing ATP, linking structure and catalytic effect.
Researchers developed a new computational methodology to simulate polyoxometalate (POM) formation, enabling the prediction of key factors and suitable conditions. This enhances the open-source tool POMSimulator, facilitating efficient processing of numerous speciation models.
Physicists at European XFEL have made comprehensive observations of ionisation processes in warm dense matter. The team observed how quickly copper transforms into the exotic state of ionised WDM to become transparent to X-rays.
Researchers develop new method to create azetidines, a stable form of heterocycle that don't oxidize under physiological conditions, expanding toolkit for drug developers to improve medication safety and reduce side effects. This breakthrough enables the creation of more useful chemical transformations with better medical functions.
Researchers have developed a novel technique using a new holmium catalyst for synthesizing hydrocarbazoles with tetrasubstituted carbon. The method uses a lanthanide-based catalyst and can be recycled, paving the way for sustainable chemical processes.
Researchers at Osaka University have developed systematically designed molecules that absorb near-infrared light but not visible light, paving the way for new applications in electronics. The new compounds show promise in areas such as solar cells, transistors, chemotherapy, and photodetectors.
A team of researchers from Tokyo Institute of Technology has developed a new type of computational RNA droplet that can perform logical AND operations using microRNA sequences. These programmable droplets have the potential to be used in various applications, including biomolecular sensing and artificial cells.
Researchers develop LoCoHD algorithm to compare protein structures based on chemical information of atoms, enabling analysis of molecular machines and identifying critical amino acids. The method shows promise in predicting protein functions, including studying the internal motion of proteins like podocin.
Researchers discovered that just eight new biochemical reactions can bridge the gap between simple geochemistry and biochemistry, indicating a limited loss of biochemistry to time. This finding suggests that even extinct reactions can be rediscovered from clues left behind in modern biochemistry.
The new materials offer an alternative to metal-organic frameworks (MOFs) and have already shown early promise for the capture of iodine. They are yet to be fully explored but hold potential for applications in proton conduction, catalysis, water capture, and hydrogen storage.