materials studio software biovia materials studio academic research version 8.0 (Accelrys)
90
Structured Review
Accelrys
materials studio software biovia materials studio academic research version 8.0
Materials Studio Software Biovia Materials Studio Academic Research Version 8.0, supplied by Accelrys, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/materials+studio+software+biovia+materials+studio+academic+research+version+8%2E0/materials+studio+software+biovia+materials+studio+academic+research+version+8+0/pm39436690-65-30-33
Average 90 stars, based on 1 article reviews
Materials Studio Software Biovia Materials Studio Academic Research Version 8.0, supplied by Accelrys, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/materials+studio+software+biovia+materials+studio+academic+research+version+8%2E0/materials+studio+software+biovia+materials+studio+academic+research+version+8+0/pm39436690-65-30-33
Average 90 stars, based on 1 article reviews
materials studio software biovia materials studio academic research version 8.0 - by Bioz Stars,
2026-10
90/100 stars
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Software:Article Title: Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives. Article Snippet: This study aimed to predict the selected antipyrine compounds’ inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment.. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables.. Using machine learning techniques such as K-nearest neighbor (KNN) and artificial neural network (ANN), a predictive model was built for 39 antipyrine compounds with known corrosion inhibition efficiencies for carbon and low alloy steel in hydrochloric acid solutions. |