AIPressRoom’s Post

AlvaDesc has been used in Pandey, S. K., & Kunal Roy (2024). Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach. Energy Advances. Abstract: The performance and stability are the two major areas of concern related to energetic materials (EMs). Balancing both the performance and stability simultaneously can result in the development of new advanced compounds that will not only perform better but at the same time be highly stable to physical/chemical/thermal stress. In this study, we aimed to predict some of the properties related to detonation performance (density, n = 12 805; gas-phase heat of formation, n = 2565) and thermal stability (decomposition temperature, n = 656; melting point, n = 19 667) of EMs using the quantitative Read-Across Structure–Property Relationship (q-RASPR) approach. q-RASPR, a combined application of quantitative structure–property relationship (QSPR) and RA methodologies, has shown an enhancement in the model predictivity, compared to the traditional QSPR method. The data sets collected from var- ious sources were first curated to prepare high-quality data. After the structural representation of the data points and descriptor calculation, each data set was divided into the respective training and test sets. Different methodologies were employed to train the model, and the models so developed were validated based on the Organization for Economic Cooperation and Development (OECD) principles. Also, the developed models’ predictivity was checked using different ML algorithms. All the developed models showed good statistical quality with R2 values (training set) ranging from 0.64 for decomposition temperature and 0.75 for the melting point to 0.94 for density and heat of formation data sets. Also, the external validation results were quite promising, which indicates that the predictive power of our developed models was significant. The models so developed can be used for examining the performance and heat resistance capacity of the newly developed compounds, screening of databases, modification of older derivatives, and/or the development of heat-resistant (non-thermo-labile) and impactful EMs. You can find more information on alvaDesc at: https://lnkd.in/gyaHaaM #researchpaper #alvadesc #moleculardescriptors #machinelearning #qsar #cheminformatics #compchem #insilico

Understanding the oxidation states of ions is crucial for inferring structure-property relationships in materials. This new electrochemical series should make that a bit easier. https://lnkd.in/gzZt4Ezq

Researchers used machine learning to predict and enhance the specific capacitance of N-doped porous biochar electrodes for supercapacitors. The Random Forest model proved most accurate, revealing that optimizing pore structure is more crucial than nitrogen doping, offering insights for better biochar electrode design. #Biochar #Pyrolysis #CarbonCapture

GIST researchers investigate strange transient responses of organic electrochemical transistors

Join the U.S. Department of Energy Bioenergy Technologies Office’s Chemical Catalysis for #Bioenergy Consortium (#ChemCatBio) on June 12, 2024, for a webinar on #ArtificialIntelligence for #catalysis. Argonne National Laboratory researcher Rajeev Assary will discuss: ✅How AI and high-fidelity, first-principles simulations can help identify cost-efficient catalysts for deoxygenation chemistry; ✅Recent efforts on using machine learning to field billions of molecules to choose the best as liquid organic hydrogen carriers; ✅Ongoing research directions for helping the catalysis community incorporate large language models in catalyst discovery. Register today! https://lnkd.in/eHmF93Qg

🌍By leveraging sophisticated simulations, we're not just imagining a greener future—we're building it with COFs. 🌱 The discovery of new Covalent Organic Frameworks (COFs) made entirely from organic salts was achieved by a research team using advanced computer simulations. This breakthrough allows for the precise prediction and design of these innovative materials, offering significant potential in various applications such as gas storage and catalysis. 🔬Covalent Organic Frameworks (COFs) are real materials that have been synthesized and studied in laboratories. They are not just theoretical simulations; their properties and potential applications have been validated through experimental research. The use of computer simulations in their design has significantly advanced our understanding and ability to create these materials with precise and desirable characteristics. #Sustainability #Innovation #EnvironmentalScience #GreenTech #ComputerSimulations #COFs

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing https://buff.ly/3XVU3J8

eChemicles is pushing the boundaries of scientific technologies! As a part of our #research on CO₂ #electrolysis we are integrating advanced approaches as cutting-edge modelling and analysis techniques to ensure our #sustainable pioneering technology’s #development. 💚 Our work covers multiple areas, including: - Computational Fluid Dynamics (CFD) Modelling: fluid flow and reaction dynamics #simulation to optimize electrolyser's architecture. - Data Analysis & Processing: applying advanced mathematical tools to analyse and process data for enhanced accuracy and #efficiency. - Empirical Models: developing data-driven models to capture real-world behaviours and improve system #performance. - AI-Assisted Models: leveraging AI to optimize predictive modelling and improve CO₂ conversion efficiency. - Process-Level Modelling: simulating and optimizing full-scale industrial processes, refining the overall system for optimal performance. eChemicles actively supports #decarbonisation and the fight against climate change by turning CO₂ as one of the most critical greenhouse gases into valuable products. ♻️ We are paving the way for a greener #future, for a Better Tomorrow! 🌍 #CCU #chemistry #GHG #climatechange #recycle #carbon #optimization

A recent study explored #ironbark #biochar as an effective, low-cost adsorbent for #gold recovery, achieving 858 mg/g adsorption from aqueous solutions. Advanced modeling favored #ArtificialNeuralNetworks for predicting outcomes, with physisorption and electrostatic attraction identified as key mechanisms. https://lnkd.in/gW3Xh8cV

Porous #adsorbents have common characteristics, such as high porosity and a large specific surface area. These characteristics, attributed to the internal structure of the material, significantly affect their #adsorption performance. We created a data set and collected data points from porous adsorbents, including carbon-based (CBM), porous polymers (POPs), metal-organic frameworks (#MOFs), and #zeolites to understand their characteristics for #CO2 adsorption.  We developed #machinelearning (#ML) #algorithms to display the CO2 adsorption performance as a function of characteristics and adsorption isotherm parameters. Special thanks to all our co-authors and Canmate ENERGY-Ottawa. Please check out our new paper titled: "𝑫𝒆𝒗𝒆𝒍𝒐𝒑𝒎𝒆𝒏𝒕 𝒐𝒇 𝒕𝒉𝒆 𝑪𝑶2 𝑨𝒅𝒔𝒐𝒓𝒑𝒕𝒊𝒐𝒏 𝑴𝒐𝒅𝒆𝒍 𝒐𝒏 𝑷𝒐𝒓𝒐𝒖𝒔 𝑨𝒅𝒔𝒐𝒓𝒃𝒆𝒏𝒕 𝑴𝒂𝒕𝒆𝒓𝒊𝒂𝒍𝒔 𝑼𝒔𝒊𝒏𝒈 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑨𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎𝒔" published in the ACS Applied Energy Materials Journal. 𝐀𝐂𝐒 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐬 link: https://lnkd.in/gSFskXQv 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐆𝐚𝐭𝐞 link: https://lnkd.in/gvPNrpeB #AI #CO2capture #Nanomaterial #materialSciense #polymer #pourosmaterial #activatedcarbon #carbonmaterial #metalorganicframeworks #material #catalyst #americanchemicalsociety #sciense #code #modeling #carbondioxide