Discovered Materials, an AI startup founded by IIT Madras alumni Advaith Sridhar and Akash Ramdas, has raised $9 million (around Rs 85 crore) in a funding round to develop AI-driven systems for discovering materials used in semiconductor manufacturing and data-centre infrastructure.
The round was led by Lightspeed India Partners, with participation from Y Combinator, Peak XV Partners, and angel investors including Paul Graham, Gokul Rajaram, and Thariq Shihipar. The funding gives the early-stage company backing from investors across India and Silicon Valley as it develops its materials-discovery platform.
Focus on Thermal Management
Discovered Materials is initially focusing on thermal-management materials, an area that has become increasingly important as AI processors generate higher levels of heat.
High-performance AI chips can create significant thermal loads within chip packages and data-centre systems. According to the company’s founders, some current AI chips can generate heat of more than 140 watts per square centimetre. Managing this heat is critical for maintaining chip performance and reliability while also influencing cooling requirements and the amount of computing capacity data-centre operators can deploy within a rack.
The startup plans to use multiple AI agents working together to identify potential materials, analyse their properties, and narrow down promising candidates. The approach is aimed at reducing the time traditionally required to move from material discovery to practical validation.
Targeting Faster Materials Discovery
Conventional materials research can involve lengthy cycles of computer simulations, material synthesis, laboratory testing, and manufacturing validation. Such processes can take years, particularly when materials need to meet highly specific performance and manufacturing requirements.
Discovered Materials aims to shorten these cycles substantially, with the goal of moving materials discovery from timelines that can extend beyond a decade to development cycles measured in months.
While thermal management is its initial focus, the company’s broader objective is to explore materials across the semiconductor technology stack rather than concentrate on a single cooling application.
Combining Materials Science and AI Expertise
The company’s founding team brings together backgrounds in materials science and artificial intelligence.
Akash Ramdas completed his doctorate and postdoctoral research at Stanford University, where his work included nanoscale electronics and advanced interconnect materials. His research has also covered computational approaches to materials design, including the use of Bayesian optimisation to identify materials that meet specific property requirements.
Advaith Sridhar previously worked as a research engineer at Luma Labs and Persona AI, focusing on areas including post-training, model evaluation, and agentic systems. He holds a master’s degree in artificial intelligence from Carnegie Mellon University and was recognised as the best outgoing student at IIT Madras.
The company’s technology is designed around a group of AI systems that work as virtual scientists, helping explore potential materials and progressively improve candidate selections for applications in semiconductor fabrication facilities and data centres.
AI Meets Semiconductor Materials Research
The funding also highlights the growing connection between AI-for-science, India’s engineering talent, and semiconductor supply-chain development.
India’s semiconductor efforts have largely centred on areas such as chip fabrication, assembly and testing, chip design, and supporting equipment ecosystems. Materials research represents an upstream component of the semiconductor industry, covering areas such as thermal interfaces, interconnect materials, dielectrics, packaging compounds, and process-compatible materials.
However, identifying a promising material computationally is only the first stage. Discovered Materials will still need to validate candidates through physical synthesis, repeatability testing, and semiconductor-process qualification before they can be considered for practical industrial use.
The new funding will support the company’s efforts to expand this work while developing the AI models and experimental systems needed to determine whether the materials identified by its platform can be manufactured consistently and meet the requirements of semiconductor and data-centre applications.
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