arXiv subjects

Osman Mamun

Publications and source records attributed to Osman Mamun.

2 recordsLinked to original sources

arXiv2025

The Denario project: Deep knowledge AI agents for scientific discovery

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

cs.AI↗

arXiv2024

Accelerated Development of Multicomponent Alloys in Discrete Design Space Using Bayesian Multi-Objective Optimisation

Bayesian optimization (BO) protocol based on Active Learning (AL) principles has garnered significant attention due to its ability to optimize black-box objective functions efficiently. This capability is a prerequisite for guiding autonomous and high-throughput materials design and discovery processes. However, its application in materials science, particularly for novel alloy designs with multiple targeted properties, remains limited. This limitation is due to the computational complexity and the lack of reliable and robust acquisition functions for multiobjective optimization. In recent years, expected hypervolume-based geometrical acquisition functions have demonstrated superior performance and speed compared to other multiobjective optimization algorithms, such as Thompson Sampling Efficient Multiobjective Optimization (TSEMO), Pareto Efficient Global Optimization (parEGO), etc. This work compares several state-of-the-art multiobjective BO acquisition functions, i.e., parallel expected hypervolume improvement (qEHVI), noisy parallel expected hypervolume improvement (qNEHVI), parallel Pareto efficient global optimization (parEGO), and parallel noisy Pareto efficient global optimization (qNparEGO) for the multiobjective optimization of physical properties in multi-component alloys. We demonstrate the impressive performance of the qEHVI acquisition function in finding the optimum Pareto front in 1-, 2-, and 3-objective Aluminium alloy optimization problems within a limited evaluation budget and reasonable computational cost. In addition, we discuss the role of different surrogate model optimization methods from a computational cost and efficiency perspective.

physics.chem-ph↗