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23.1 MB
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D94D5AF4B5CD8E59EBD875EAD0BEB3C7B351B575
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June 21, 2025, 12:02 p.m.
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(Last updated: June 21, 2025, 12:03 p.m.)
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| ['Eidel B. GPT for Python-Coding in Computational Materials Science..Mechanic.2025.pdf'] | 0 bytes |
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23.1 MB
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2025-06-21
| Uploaded by andryold1 | Size 23.1 MB | Health [ 20 /38 ] | Added 2025-06-21 |
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| Uploaded by indexFroggy | Size 65.6 MB | Health [ 15 /3 ] | Added 2024-10-14 |
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SOURCE: Eidel B. GPT for Python-Coding in Computational Materials Science..Mechanic.2025
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COVER
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MEDIAINFO
Textbook in PDF format This book covers all the topics about ChatGPT required to successfully generate Python code to solve problems in computational materials science and mechanics, complemented by numerous fully worked-out applications. The complete work flow for AI-assisted coding is given, including: (i) prompt engineering providing a powerful toolset for how to give coding assignments to ChatGPT effectively; (ii) commented code listings; and (iii) tips and tricks to verify the codes in rigorous tests including human interventions to fix issues and gaps. Finally, (iv) the coding projects are critically reviewed to address the strengths and remaining weaknesses of the Chatbot, including explicit recommendations on how to communicate with GPT. For the steps (i)–(iv) the book presents a curated selection of intriguing problems from computational materials science and computational mechanics including Machine Learning for problem-solving. These problems are carefully chosen for their relevance to current research and industrial applications and their suitability for showcasing the advanced capabilities of GPT-4 for code generation. Spanning from predicting material behavior under various conditions to simulating complex mechanical interactions, the problems serve as a canvas on which GPT-4 paints its solutions, demonstrating not just accuracy but creativity in problem-solving. Therefore, the book serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. Topics of Computational Materials Science Generation of Atomic Scale Single Crystals Molecular Dynamics Simulation of Noble Gases Phase Field Modeling of Grain Growth Modeling Corrosion Using a Cellular Automaton Instationary Heat Conduction on Rectangular Domains with Arbitrary Circular Holes Topics of Deep Learning Based Materials Science Transfer Learning for Alloy Classification Based on Microstructure Images Transfer Learning for Microstructure Image Segmentation Topics of Computational Analysis of Waves and Fluid Mechanics Elastic Wave Propagation Electromagnetic Wave Propagation in Dielectric Media Flow Around an Obstacle Using the Lattice Boltzmann Method Conclusions Learned Lessons-Recommendations
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