Atharv Gyan

Quick hook. What if algorithms could propose a new drug candidate, predict a protein’s 3-D shape , and run thousands of experiments in an automated lab all in the time it would take a human team to run a few? That is not sci-fi. Generative AI is already part of real scientific workflows in 2025, and this guide explains how , why , what works , what doesn’t , and how you can learn and experiment with these tools. 1. Plain-language definition: what is “generative AI for science”? Generative AI are machine-learning models that create   they generate text, images, molecular graphs, 3-D structures, or even experimental plans instead of only classifying or labeling. In scientific discovery these models are used to: propose new hypotheses, molecules or materials; predict structures and behavior (e.g., protein folding); design experiments and optimize conditions; run closed loops with robots that execute experiments and feed back results. These systems don’t replace sc...

Abstract Neurosymbolic AI represents a transformative approach in artificial intelligence research by uniting the powerful pattern recognition capabilities of deep learning with the clarity and logical structure of symbolic reasoning . This fusion promises to overcome the “ black-box ” limitations of conventional neural networks while enhancing interpretability, transparency, and decision‐making. In this article, we introduce neurosymbolic AI, trace its historical evolution, detail its key components, and explore its applications and challenges. By offering a clear, accessible explanation of this emerging field, we aim to provide readers with insights into how neurosymbolic systems can drive the next generation of explainable AI .  1. Introduction The landscape of artificial intelligence (AI) has evolved rapidly over the past few decades. Early approaches in AI predominantly relied on symbolic methods systems built on logic, rules, and human‑defined knowledge representations. I...