Related references
Note: Only part of the references are listed.Improving the CFPP property of biodiesel via composition design: An intelligent raw material selection strategy based on different machine learning algorithms
Ziheng Cui et al.
RENEWABLE ENERGY (2021)
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
James E. Saal et al.
Annual Review of Materials Research (2020)
Machine learning glass transition temperature of polymers
Yun Zhang et al.
HELIYON (2020)
Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning
Tingli Liu et al.
JOURNAL OF MATERIALS CHEMISTRY A (2020)
Mining structure-property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks
Yixing Wang et al.
MOLECULAR SYSTEMS DESIGN & ENGINEERING (2020)
Intelligent Machine Learning: Tailor-Making Macromolecules
Yousef Mohammadi et al.
POLYMERS (2019)
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design
Teng Zhou et al.
ENGINEERING (2019)
Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers
Ghanshyam Pilania et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures
Anurag Jha et al.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2019)
PubChem 2019 update: improved access to chemical data
Sunghwan Kim et al.
NUCLEIC ACIDS RESEARCH (2019)
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
Chiho Kim et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2018)
Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling et al.
SCIENCE (2018)
Prediction of viscosity index and pour point in ester lubricants using quantitative structure-property relationship (QSPR)
Shima Ghanavati Nasab et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)
NanoMine schema: An extensible data representation for polymer nanocomposites
He Zhao et al.
APL MATERIALS (2018)
Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
Raquel Rodriguez-Perez et al.
ACS OMEGA (2017)
Petro-Based and Bio-Based Plasticizers: Chemical Structures to Plasticizing Properties
Maeva Bocque et al.
JOURNAL OF POLYMER SCIENCE PART A-POLYMER CHEMISTRY (2016)
Discovery and Optimization of Materials Using Evolutionary Approaches
Tu C. Le et al.
CHEMICAL REVIEWS (2016)
Low-Molecular-Weight Glycerol Esters as Plasticizers for Poly(vinyl chloride)
Oscar Yesid Suarez Palacios et al.
JOURNAL OF VINYL & ADDITIVE TECHNOLOGY (2014)
Cross-validation pitfalls when selecting and assessing regression and classification models
Damjan Krstajic et al.
JOURNAL OF CHEMINFORMATICS (2014)
Natural-based plasticizers and biopolymer films: A review
Melissa Gurgel Adeodato Vieira et al.
EUROPEAN POLYMER JOURNAL (2011)
A QSPR for the plasticization efficiency of polyvinylchloride plasticizers
Mridula Chandola et al.
JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2008)
Influence of polyesterurethane plasticizer on the kinetics of poly(vinyl chloride) decomposition process
K Pielichowski et al.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY (2006)
Virtual computational chemistry laboratory - design and description
IV Tetko et al.
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2005)
The plasticizer market: an assessment of traditional plasticizers and research trends to meet new challenges
M Rahman et al.
PROGRESS IN POLYMER SCIENCE (2004)
How about alternatives to phthalate plasticizers?
LG Krauskopf
JOURNAL OF VINYL & ADDITIVE TECHNOLOGY (2003)