Articles tagged with Computational Chemistry
New York University researchers have trained an AI model to predict stable tautomers in drug-like molecules, addressing a challenge in molecular design. The model, released as an open-source tool, can analyze large molecular libraries and identify correct tautomer assignments.
Scientists systematically map the Biginelli reaction to uncover a previously unknown branch that produces complex bicyclic structures and molecules with unusual supramolecular behavior
Researchers at HKU have developed a new light-driven method for constructing three-dimensional molecular building blocks. This approach broadens the range of starting materials and suppresses unwanted polymerisation, overcoming key limitations of existing synthetic methods. The findings have the potential to improve characteristics of ...
The Skala AI model, developed by Microsoft Research AI for Science, is now available through the CP2K software ecosystem. CASUS and Microsoft Research collaborated to integrate Skala into CP2K, enabling more accurate quantum mechanical simulations of larger molecular systems. The collaboration aims to improve the accuracy and efficienc...
A multidisciplinary project combines computer modeling, particle science, and pharmaceutical science to create more effective inhalers for people who can't inhale deeply. Researchers found that improved designs can reduce deposition in the mouth and throat, increase penetration of particles into the lungs.
Fluctuations in nanopore structure are key to efficient molecule separation, according to a new study. The research found that dynamic 'breathing' motions can selectively accelerate the transport of one molecule over another, leading to improved separation performance.
Researchers at the University of Manchester have developed a family of tellurium-based catalysts that use chalcogen bonding to control reaction outcomes. The study successfully demonstrated the ability of neutral chalcogen-bond donors to induce asymmetry in chemical reactions, achieving enantiomeric ratios of up to 89:11.
Researchers designed an end-to-end workflow to identify new blue OLED materials using AI and quantum chemistry. They developed a virtual library of over 19,000 molecules and used machine learning to select promising candidates, which were then experimentally evaluated and found to have high color purity and efficiency.
Tosyl groups act as a form of molecular code, enabling pseudorotaxane-like geometries and biased macrocyclization reactions. The number of tosyl units determines higher-order assembly behavior, including ring closure and polymeric chain formation.
Researchers unveiled a technique to build ultra-clean 2D heterostructures using muscovite crystals, eliminating microscopic residues that disrupt electronic device performance. This method enables precise stacking of atomic layers, leading to new properties and potential breakthroughs in quantum computing and nanoelectronics.
Researchers at The University of Osaka used AI to evaluate characterization frameworks for molecular order in liquid water. They found that machine learning models can accurately capture key structural information, shedding light on the relationship between structural fluctuations and thermodynamic states of water.
Researchers at Texas A&M University develop a laser technique called TRIP to directly measure quantum forces shaping proteins, enabling accurate prediction of how pharmaceutical drugs interact with them. This breakthrough could lead to the design of medicines tailored to specific diseases, revolutionizing precision medicine.
Scientists at the Simons Foundation's Flatiron Institute developed a new method that enables faster simulations of molecular behavior without sacrificing accuracy. The breakthrough could significantly reduce time and energy demands for simulations, impacting fields such as material design and drug interactions.
Researchers discovered that lithium doping of a 12-benzene-ring molecule creates a material with strong optical responses due to synergistic effects between aromaticity and charge transfer. This finding establishes fundamental design principles for high-performance carbon-based photonic devices.
A new study uses AI to identify promising chemical compounds that could develop into effective antibiotics against multi-drug resistant Neisseria gonorrhoeae. The approach has the potential to address the growing crisis of antimicrobial resistance in this fast-evolving pathogen.
MDNA, an open-source software suite, enables accurate models of DNA structures and simulations. It facilitates visualization and analysis of DNA-protein interactions, improving understanding of DNA dynamics in complex biological systems.
A new quantum chemistry method predicts the behavior of molecules under light with lower computational cost, enabling the study of larger systems and complex reaction pathways. This breakthrough advances the discovery of next-generation materials and deepens understanding of molecular behavior under light.
Researchers from Tohoku University's Advanced Institute for Materials Research use AI and data science to extract valuable insights from decades-old experiments and scientific literature. This approach accelerates materials design and screening in catalysis, solid-state electrolytes, and hydrogen storage research.
Gagliardi's work on multireference electronic-structure theory has led to breakthroughs in catalysis, carbon capture, and water harvesting. Her development of MC-PDFT and LAS methods has facilitated the design of materials that function as molecular sponges.
Researchers develop quantum algorithms to simulate polymer degradation caused by UV radiation, using industrially relevant aircraft coatings as an example. The goal is to optimize surface coatings for various industries, improving safety and reducing costs.
Researchers from the University of Jyväskylä emphasize the importance of multiscale modeling in understanding and developing electrochemical processes. The study highlights the need for a thorough understanding of methods used and reactions studied, as computational tools can reliably model phenomena on different scales.
Researchers used AI to design new molecules for disinfectants, leveraging a dataset of hundreds of existing quaternary ammonium compounds. The approach yielded 11 promising compounds with activity against antimicrobial-resistant bacteria, offering a potential solution to the growing threat of 'superbugs'.
A computational protocol has been established by University of Kent researchers to accurately identify reactions that can result in successful drug candidates for Chagas disease. This approach reduces the need for trial-and-error, prioritizing promising compounds earlier and making the drug discovery process faster and more affordable.
Researchers discover quasi-one-dimensional superionic state of carbon hydride under extreme pressures and temperatures found deep inside ice giant planets. This finding has implications for heat and electricity movement through planetary interiors and could influence magnetic-field generation.
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 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.
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.
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.