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Materials Informatics I : Methods / / edited by Kunal Roy, Arkaprava Banerjee
| Materials Informatics I : Methods / / edited by Kunal Roy, Arkaprava Banerjee |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XVII, 288 p. 66 illus., 53 illus. in color.) |
| Disciplina | 542.85 |
| Collana | Challenges and Advances in Computational Chemistry and Physics |
| Soggetto topico |
Cheminformatics
Materials Chemistry Computer simulation Machine learning Artificial intelligence Computational Design Of Materials Machine Learning Artificial Intelligence |
| ISBN | 3-031-78736-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Introduction -- Introduction to Materials Informatics -- Introduction to Cheminformatics for Predictive Modeling -- Introduction to machine learning for predictive modeling of organic materials -- Quantitative Structure-Property Relationships (QSPR) for Materials Science -- Part 2. Methods and Tools -- Quantitative Structure-Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science -- Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling -- In silico QSPR studies based on CDFT and IT descriptors -- Applications of quantitative read-across structure-property relationship (q-RASPR) modeling in the field of materials science -- Machine Learning algorithms for applications in Materials Science I -- Machine Learning algorithms for applications in Materials Science II -- Structure-property modeling of quantum-theoretic properties of benzenoid hydrocarbons by means of connection-related graphical descriptors -- Machine learning tools and Web services for Materials Science modelling. |
| Record Nr. | UNINA-9910993940403321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Materials Informatics II : Software Tools and Databases / / edited by Kunal Roy, Arkaprava Banerjee
| Materials Informatics II : Software Tools and Databases / / edited by Kunal Roy, Arkaprava Banerjee |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XVI, 297 p. 102 illus., 95 illus. in color.) |
| Disciplina | 542.85 |
| Collana | Challenges and Advances in Computational Chemistry and Physics |
| Soggetto topico |
Cheminformatics
Materials Chemistry Computer simulation Machine learning Artificial intelligence Computational Design Of Materials Machine Learning Artificial Intelligence |
| ISBN | 3-031-78728-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Introduction -- Introduction to Machine Learning for Predictive Modeling I -- Introduction to Machine Learning for Materials Property Modeling -- Part 2. Cheminformatic and Machine Learning Models for Nanomaterials -- Machine learning models to study electronic properties of metal nanoclusters -- Applications of Machine Learning Predictive Modeling for Carbon Quantum Dots -- Assessing the toxicity of quantum dots in healthy and tumoral cells with ProtoNANO, a platform of nano-QSAR models to predict the toxicity of inorganic nanomaterials -- Applications of predictive modeling for fullerenes -- Computational Analysis of Perovskite Materials AlXY3 (X = Cu, Mn; Y = Br, Cl, F) invoking the DFT Method -- Applications of predictive modeling for dye-sensitized solar cells (DSSCs) -- Introduction to multiscale modeling for One Health approaches -- DIAGONAL Decision Support System (DSS) for Advanced Nanomaterial Risk Management powered by Enalos Cloud Platform -- Part 3. Software Tools and Databases for Applications in Materials Science -- Machine Learning algorithms, tools, and databases for applications in Materials Science -- Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules. |
| Record Nr. | UNINA-9910987695603321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Materials Informatics III : Polymers, Solvents and Energetic Materials / / edited by Kunal Roy, Arkaprava Banerjee
| Materials Informatics III : Polymers, Solvents and Energetic Materials / / edited by Kunal Roy, Arkaprava Banerjee |
| Autore | Roy Kunal |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (550 pages) |
| Disciplina | 542.85 |
| Altri autori (Persone) | BanerjeeArkaprava |
| Collana | Challenges and Advances in Computational Chemistry and Physics |
| Soggetto topico |
Cheminformatics
Machine learning Materials Catalysis Force and energy Materials science - Data processing Nanochemistry Machine Learning Materials for Energy and Catalysis Computational Materials Science |
| ISBN |
9783031787249
9783031787232 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Introduction -- Introduction to Machine Learning for Predictive Modeling II -- Introduction to predicting properties of organic materials -- Part 2. Cheminformatic and Machine Learning Models for Polymers -- Machine Learning Applications in Polymer Informatics – An Overview -- Applications of predictive modeling for selected properties of polymers -- Polymer Property Prediction using Machine Learning -- Applications of predictive modeling for polymers -- Part 3. Cheminformatic and Machine Learning Models for Solvents -- Applications of predictive QSPR modeling for deep eutectic solvents -- Applications of predictive modeling for various properties of ionic liquids -- Part 4. Cheminformatic and Machine Learning Models for Energetic Materials -- Improving Safety with Molecular-Scale Computational Approaches for Energetic and Reactive Materials -- Predictive modeling for energetic materials -- Modeling the performance of energetic materials -- Applications of predictive modeling for energetic materials. |
| Record Nr. | UNINA-9910984592403321 |
Roy Kunal
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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q-RASAR : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee
| q-RASAR : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee |
| Autore | Roy Kunal <1971-> |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (99 pages) |
| Disciplina | 542.85 |
| Collana | SpringerBriefs in Molecular Science |
| Soggetto topico |
Chemistry - Data processing
Quantum theory Computer simulation Molecules - Models Computational Chemistry Quantum Simulations Molecular Modelling |
| ISBN | 3-031-52057-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chemical Information and Molecular Similarity -- Read-across and Quantitative Structure-activity Relationships (QSAR) for Making Predictions and Data Gap-Filling -- Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure-Activity Relationships (q-RASAR) – Genesis and Model Development -- Tools, Applications, and Case Studies (q-RA and q-RASAR) -- Future Prospects. |
| Record Nr. | UNINA-9910806198703321 |
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Roy Kunal <1971->
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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