Post by Software for Chemistry & Materials

𝗣𝗼𝗹𝘆𝗺𝗲𝗿 𝗱𝗶𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗹𝗼𝘀𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 in electrical insulation, electronic packaging, interlayer dielectrics, printed wiring boards, and related materials. 𝗘𝗮𝗿𝗹𝘆-𝘀𝘁𝗮𝗴𝗲 𝘀𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱 because measured tangent loss depends on frequency, temperature, morphology, and polymer details. Our first 𝗔𝗠𝗦 𝗜𝗱𝗲𝗮𝘀 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻 notebook explores 𝗮 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁: use AMS/PLAMS and DFTB calculations to generate quantum-chemical descriptors for polymer proxy structures, then use those descriptors in a 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗳𝗼𝗿 𝘁𝗮𝗻𝗴𝗲𝗻𝘁-𝗹𝗼𝘀𝘀 𝗱𝗮𝘁𝗮. The notebook is inspired by a 𝟮𝟬𝟬𝟴 𝗤𝗦𝗣𝗥 𝘀𝘁𝘂𝗱𝘆 𝗯𝘆 𝗬𝘂 𝗮𝗻𝗱 𝗰𝗼𝘄𝗼𝗿𝗸𝗲𝗿𝘀. It is meant for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴: inspect the workflow, run it, adapt it, and extend it to related 𝗽𝗼𝗹𝘆𝗺𝗲𝗿 𝘀𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗽𝗼𝘀𝘁 𝗮𝗻𝗱 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸: https://lnkd.in/ef4sW8d3 #ComputationalChemistry #CompChem #MaterialsScience #Polymers #MachineLearning #QSPR