Ypso-Facto’s Post

Discovery Studio Simulation | Simulate Biological Systems with Best-in-Class Tools Providing insight into the mechanism of action and properties, simulation can help elucidate the energetics of biomolecular processes, which rely on a variety of dynamic interactions between proteins, ligands, solvents, and ions. Often, the specifics of these interactions are difficult to capture via physical experimentation alone due to the short time scales over which they occur. Read More: https://lnkd.in/gu-fbb6i #BiomolecularProcesses #DynamicInteractions #SimulationInsights #altem Dassault Systèmes BIOVIA Dhivya S Dr. Sakshi Bhardwaj

Check out our new tutorial on Cross-linking reaction of an epoxy resin ! This tutorial explains the steps required to perform reaction calculations with the reaction modeler, using as an example the cross-linking reaction of an epoxy resin. ※Reaction calculation needs VSOP license. #J-OCTA - Molecular Dynamics Simulation Software / Reaction Modeler is used to model the tutorial. About J-OCTA _______________ J-OCTA can be used at the forefront of materials and life science R&D J-OCTA is a multi-scale simulation software that predicts properties from the atomistic to the micrometer scale on a computer for research and development of a wide range of high-performance materials such as resins, rubbers, nanocomposites, thin films, inks, and batteries, and in the life science field such as drug discovery and pharmaceutical formulation. It can be used as a knowledge discovery tool to understand the mechanisms of complex phenomena that could not be understood by experiments alone, and as a data generation tool for informatics. From molecular characteristics to material properties - First-Principles calculation (engine name: SIESTA) - Molecular dynamics simulation (engine name: COGNAC, VSOP, GENESIS) - Interface/phase separation simulation (engine name: SUSHI, COGNAC-DPD) - Rheology simulation (engine name: VSOP-DPD, PASTA, NAPLES) - Multi-phase material, RVE, and Process simulation (engine name : MUFFIN, VSOP-PS) The modeling tools provided in J-OCTA for each engine are introduced here. Interfaces with other software, the QSPR function for the simplified calculation of polymer properties, and inter-scaling coupling functions - including zooming and reverse mapping - are available. #Crosslinking #MolecularDynamics #Simulation #DHIO #DHIORESEARCH J-OCTA DHIO Research & Engineering Pvt Ltd.,

We're eagerly counting down the days until the CGOM15 conference, which is now less than a week away! Make sure to catch Alvin's highly anticipated talk: "The talk will showcase the importance of crystal shape engineering. This represents a fundamental aspect of any industry that produces or handles solid matter, including the pharmaceuticals, agrochemicals, energy, and electronics industries. In this work, a powerful new automated protocol will be presented that rapidly optimises the free energies of intermolecular interactions for use within a Monte Carlo (MC) model such as CrystalGrower for a wide range of molecular crystalline solids, thereby removing a major bottleneck to the study of new materials. Depiction of an optimisation process through snapshots as the shape approaches the global minimum set by a reference crystal shape." - Alvin Walisinghe #CGOM15 #crystalshape #prediction #crystalgrower #optimisation #conference #topography

I am very proud of my team who launched this #TDA #Challenge to demonstrate the power and precision of TaylorSizer! You can also take advantage of the TDA Challenge if you want to compare your DLS results with the TDA method on a sample for which you would like a better resolution! Our technology provides molecular size distribution, viscosity and stability of nano-objects < 50 nm. 🔗 hashtag#nanotechnology hashtag#TaylorSizer hashtag#ScientificInnovation hashtag#NanoObjects hashtag#TaylorDispersionAnalysis

Join Sartorius, Art Hamfeldt, and Johan Hultman at The Federation of Analytical Chemistry and Spectroscopy Societies (FACSS) and the SciXConference Presented by FACSS in Raleigh, NC for an interesting workshop on "Sample Selection Design of Experiments (DOE) for Multivariate Calibration". This course will cover the use of Design of Experiments (DOE) for sample selection for the development of multivariate calibration models. Matrixes are becoming more challenging as the desire to deploy spectroscopy has increased. Those new to building multivariate calibration models have a tendency to build oversampled models with samples that are completely overlapping between instruments, probes, and/or matrix solutions. The DOE approach discussed in this course can help identify the minimum number of samples required, thus resulting in saving time and resources while building more robust models.  https://lnkd.in/d3EAY5_g Monday, Oct 21 8:30am – 12:00pm SciX 136 - Sample Selection Design of Experiments (DOE) for Multivariate Calibration

DP Technology has introduced a pioneering advancement in battery technology with the Uni-ELF Universal Electrolyte Formulation Framework. This innovative AI-driven approach enhances electrolyte design by significantly improving predictions of molecular properties (e.g., melting point, boiling point, synthesizability) and formulation attributes (e.g., conductivity, Coulombic efficiency) compared to existing methods. Uni-ELF achieves this through a dual-stage pre-training process: leveraging the Uni-Mol model to reconstruct 3D molecular structures and using molecular dynamics simulations to predict statistical structural properties at the formulation level. This comprehensive approach allows Uni-ELF to capture complex molecular and mixture-level information, thereby enhancing its predictive accuracy. #BatteryTechnology #ElectrolyteDesign #ResearchAndDevelopment #QimingPortfolio #QimingHealthcare

I have gained a lot in the company this week. What impressed me the most was that the Gator Bio team has improved the quality of Biosensor by an order of magnitude. My understanding is that in order to make a good product, we must first understand the principle and work hard from the root. This reminds me of the ion focusing principle of mass spectrometry, AB Sciex’s Quadrupole MS uses electrostatic lenses focus ions into the quadrupole rods; Agilent’s TOF mass spectrometer accelerates ions by a strong electric field and separated based on flight time; Thermo Fisher’s Ion Trap mass spectrometer traps ions in a 3-D electric field and ejected sequentially, however it’s Orbitrap mass spectrometer injects ions directly into a central, rotating ring electrode and trapped in an orbital motion. Compared to mass spectrometers, current BLI applications only scratch the surface. What’s the next innovation? #GatorBio #BLI #MS #IonFocus #ABSciex #Agilent #ThermoFisher #Quadrupol #TOF #IonTrap #Orbitrap

Our latest study in collaboration with the Noah Malmstadt uses machine learning to analyze in-line spectroscopic data in flow reactors to measure the nucleation and growth kinetics for nanocrystals that possess featureless absorption spectra. In this proof-of-concept demonstration, we demonstrate how two different ionic liquid solvents affect Pt nanocrystal size and yield. https://lnkd.in/gj6hJGZy

📢 New preprint out! We constrain the molecular generation space to follow the "symmetry" of patented molecules that are likely to be synthesizable. We achieved this with "symmetry-aware" fragment decomposition, and a constrained Monte Carlo Tree Search generator. The "symmetry" constraint has two effects: 1) Serves as an approximate form of synthesizability 2) Reduces hallucination tendency of generator and OOD unreliability of property reward predictor This work was done with James Damewood, Juno Nam, Kevin Greenman, Avni S., and Rafael Gómez Bombarelli with funding from Sumitomo Chemical

🧑🏾🔬👩🏻🔬Do you sometimes feel overwhelmed 🤯with huge number of samples required to design the best instrument / probe / matrix for your spectroscopy analysis ? Save the date 📆 October 21 and learn how a Design of Experiments (DOE) approach can help identify the minimum number of samples required, thus resulting in saving time and resources while building more robust models.