https://cekuma.com/blog Articles by Chinedu Ekuma on machine learning, LLMs, generative AI, and computational materials science. en-us Tue, 22 Jul 2025 12:00:00 GMT hi@cekuma.com (Chinedu Ekuma) hi@cekuma.com (Chinedu Ekuma) https://cekuma.com/blog/generative-models-materials-discovery https://cekuma.com/blog/generative-models-materials-discovery Tue, 22 Jul 2025 12:00:00 GMT AI/Materials Science GNoME, MatterGen, and universal interatomic potentials have moved inorganic materials discovery from library screening to targeted generation. What this means for R&D leaders in 2025. Chinedu Ekuma https://cekuma.com/blog/rag-for-scientific-research https://cekuma.com/blog/rag-for-scientific-research Thu, 12 Jun 2025 12:00:00 GMT AI/LLM Why plain LLMs fail on scientific questions, how RAG closes the grounding gap, and the architectural decisions that separate a demo from a system researchers actually trust. Chinedu Ekuma https://cekuma.com/blog/langgraph-runtime-learning https://cekuma.com/blog/langgraph-runtime-learning Thu, 13 Feb 2025 12:00:00 GMT AI/LLM Exploring MemRL and how reinforcement learning over episodic memory enables agents to improve at runtime without retraining—keeping the LLM frozen while the memory evolves. Chinedu Ekuma https://cekuma.com/blog/langgraph-agent-memory https://cekuma.com/blog/langgraph-agent-memory Thu, 13 Feb 2025 12:00:00 GMT AI/LLM How LangGraph enables truly stateful, production-grade AI agents through graph-based orchestration, durable checkpointing, and hybrid short-term and long-term memory architectures. Chinedu Ekuma https://cekuma.com/blog/ai-materials-lab-to-industry https://cekuma.com/blog/ai-materials-lab-to-industry Thu, 09 Jan 2025 12:00:00 GMT AI/Materials Science How high-throughput experimentation, machine learning, and self-driving labs are bridging the gap between laboratory discovery and industrial-scale materials deployment. Chinedu Ekuma https://cekuma.com/blog/ai-materials-serendipity-to-systems https://cekuma.com/blog/ai-materials-serendipity-to-systems Thu, 09 Jan 2025 12:00:00 GMT AI/Materials Science Exploring how AI is transforming materials discovery from trial-and-error to intelligent, systematic exploration—compressing the search space where intuition can be applied. Chinedu Ekuma https://cekuma.com/blog/llm-materials-extraction https://cekuma.com/blog/llm-materials-extraction Sun, 15 Dec 2024 12:00:00 GMT AI/LLM How PropertyExtractor leverages GPT-4 and Gemini Pro with zero-shot and few-shot learning to extract and verify material property data from scientific literature. Chinedu Ekuma https://cekuma.com/blog/neuromorphic-computing-materials https://cekuma.com/blog/neuromorphic-computing-materials Thu, 28 Nov 2024 12:00:00 GMT Materials Science Our research on functionalizing 2D materials with organic molecules to create new materials with potential for neuromorphic computing applications. Chinedu Ekuma https://cekuma.com/blog/ml-elastic-properties https://cekuma.com/blog/ml-elastic-properties Sun, 10 Nov 2024 12:00:00 GMT Machine Learning A deep dive into our machine learning approach for predicting elastic and mechanical properties of 2D materials, published in Scientific Reports. Chinedu Ekuma https://cekuma.com/blog/quantum-solar-efficiency https://cekuma.com/blog/quantum-solar-efficiency Fri, 25 Oct 2024 12:00:00 GMT Quantum Physics Exploring how quantum materials could potentially surpass the theoretical efficiency limits of traditional solar cells through novel electronic properties. Chinedu Ekuma https://cekuma.com/blog/correlated-electron-systems https://cekuma.com/blog/correlated-electron-systems Tue, 08 Oct 2024 12:00:00 GMT Condensed Matter An introduction to strongly correlated materials and why they exhibit fascinating properties like high-temperature superconductivity and colossal magnetoresistance. Chinedu Ekuma https://cekuma.com/blog/first-principles-calculations https://cekuma.com/blog/first-principles-calculations Fri, 20 Sep 2024 12:00:00 GMT Computational Physics How density functional theory and ab initio methods enable us to predict material properties from quantum mechanics without experimental input. Chinedu Ekuma