Cover Image

Developing an AI Course for Synthetic Chemistry Students

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Zheng, Zhiling
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Materials Science Physics Education
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era
by: Coyle, Reid A., et al.
Published: (2026)

A computational materials science paradigm for a Course-based Undergraduate Research Experience (CURE)
by: Strubbe, David A.
Published: (2024)

Image and Data Mining in Reticular Chemistry Using GPT-4V
by: Zheng, Zhiling, et al.
Published: (2023)

The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
by: Duan, Chenru, et al.
Published: (2025)

AI-accelerated Discovery of Altermagnetic Materials
by: Gao, Ze-Feng, et al.
Published: (2023)

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
by: Pham, Thang D., et al.
Published: (2025)

Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
by: Walther, Peter, et al.
Published: (2026)

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education
by: Wang, Karen D., et al.
Published: (2024)

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols
by: Soleymanibrojeni, Mohammad, et al.
Published: (2025)

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research
by: Huang, Haonan
Published: (2026)

AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment
by: Ridwan, Osman Goni, et al.
Published: (2025)

AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture
by: Garbelotto, Davide, et al.
Published: (2026)

MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration
by: Ni, Ziqi, et al.
Published: (2024)

AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
by: Biryukov, Vsevolod, et al.
Published: (2026)

Qumus: Realization of An Embodied AI Quantum Material Experimentalist
by: Shi, Lihan, et al.
Published: (2026)

AI-Guided Quantum Material Simulator for Education. Case Example: The Neuromorphic Materials Calculator 2025
by: Barrionuevo, Santiago D., et al.
Published: (2025)

InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
by: Han, Xiao-Qi, et al.
Published: (2024)

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
by: Roy, Aritra, et al.
Published: (2026)

MEIDNet: Multimodal generative AI framework for inverse materials design
by: Babu, Anand, et al.
Published: (2026)

A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
by: Zhu, Yufan, et al.
Published: (2024)

Relevancy in Basic Courses: Considering Toxic Chemical Disposal.
by: Sollimo, Vincent J.
Published: (1985)

System of Agentic AI for the Discovery of Metal-Organic Frameworks
by: Inizan, Theo Jaffrelot, et al.
Published: (2025)

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
by: Zhou, Lianhao, et al.
Published: (2025)

Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning
by: Miryashkin, Timofei, et al.
Published: (2025)

Benchmarking Universal Interatomic Potentials on Zeolite Structures
by: Ito, Shusuke, et al.
Published: (2025)

Artificial Intelligence and Generative Models for Materials Discovery -- A Review
by: Handoko, Albertus Denny, et al.
Published: (2025)

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
by: Kuner, Matthew C., et al.
Published: (2025)

On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimization
by: Kulaev, Kirill, et al.
Published: (2025)

Establishing baselines for generative discovery of inorganic crystals
by: Szymanski, Nathan J., et al.
Published: (2025)

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
by: Subramanian, Akshay, et al.
Published: (2024)

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
by: Ji, Chenyi, et al.
Published: (2026)

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
by: Yan, Keqiang, et al.
Published: (2024)

Towards grounded autonomous research: an end-to-end LLM mini research loop on published computational physics
by: Huang, Haonan
Published: (2026)

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
by: Grizzi, Vitor F., et al.
Published: (2026)

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
by: Kotama, Takanori, et al.
Published: (2026)

Symmetry-Driven Generation of Crystal Structures from Composition
by: Yin, Shi, et al.
Published: (2026)

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
by: Barroso-Luque, Luis, et al.
Published: (2024)

Universal Machine Learning Kohn-Sham Hamiltonian for Materials
by: Zhong, Yang, et al.
Published: (2024)

Deep Learning Models for Colloidal Nanocrystal Synthesis
by: Gu, Kai, et al.
Published: (2024)

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians
by: Zhong, Yang, et al.
Published: (2026)