13 citations · 20 across the 13 of their papers we have counts for
6 papers · 1 filter
LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs
Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai +2
Scientific discovery is a closed-loop process in which hypotheses guide data acquisition and observations refine the hypothesis space. Yet most approaches reduce discovery to super…
Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines
Aditya Choudhary, Saaketh Desai, Prasad Iyer
Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. H…
LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery
Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai +1
Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials…
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery
Saaketh Desai, Sadhvikas Addamane, Jeffrey Y. Tsao +4
Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laborator…
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen, Khemraj Shukla, Saaketh Desai +2
Direct numerical simulations (DNS) are accurate but computationally expensive for predicting materials evolution across timescales, due to the complexity of the underlying evolutio…
Parsimonious neural networks learn interpretable physical laws
Saaketh Desai, Alejandro Strachan
Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its…