arXiv subjects

Manav Ramprasad

Publications and source records attributed to Manav Ramprasad.

3 recordsLinked to original sources

arXiv2025

El Agente Cuantico: Automating quantum simulations

Quantum simulation is central to understanding and designing quantum systems across physics and chemistry. Yet it has barriers to access from both computational complexity and computational perspectives, due to the exponential growth of Hilbert space and the complexity of modern software tools. Here we introduce{\cinzel El Agente Cu\'antico}, a multi-agent AI system that automates quantum-simulation workflows by translating natural-language scientific intent into executed and validated computations across heterogeneous quantum-software frameworks. By reasoning directly over library documentation and APIs, our agentic system dynamically assembles end-to-end simulations spanning state preparation, closed- and open-system dynamics, tensor-network methods, quantum control, quantum error correction, and quantum resource estimation. The developed system unifies traditionally distinct simulation paradigms behind a single natural-language interface. Beyond reducing technical barriers, this approach opens a path toward scalable, adaptive, and increasingly autonomous quantum simulation, enabling faster exploration of physical models, rapid hypothesis testing, and closer integration between theory, simulation, and emerging quantum hardware.

quant-ph↗

arXiv2025

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery

In recent years, pre-trained graph neural networks (GNNs) have been developed as general models which can be effectively fine-tuned for various potential downstream tasks in materials science, and have shown significant improvements in accuracy and data efficiency. The most widely used pre-training methods currently involve either supervised training to fit a general force field or self-supervised training by denoising atomic structures equilibrium. Both methods require datasets generated from quantum mechanical calculations, which quickly become intractable when scaling to larger datasets. Here we propose a novel pre-training objective which instead uses cheaply-computed structural fingerprints as targets while maintaining comparable performance across a range of different structural descriptors. Our experiments show this approach can act as a general strategy for pre-training GNNs with application towards large scale foundational models for atomistic data.

cond-mat.mtrl-sci↗

arXiv2019

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

The success of the Materials Genome Initiative has led to opportunities for data-driven approaches for materials discovery. The recent development of Polymer Genome (PG), which is a machine learning (ML) based data-driven informatics platform for polymer property prediction, has significantly increased the efficiency of polymer design. Nevertheless, continuous expansion of the "training data" is necessary to improve the robustness, versatility and accuracy of the ML predictions. In order to test the performance and transferability of the predictive models presently available in PG (which were previously trained on a dataset of 450 polymers), we have carefully collected additional experimental glass transition temperature (Tg) data for 871 polymers from multiple data sources. The Tg values predicted by the present PG models for the polymers in the newly collected dataset were compared directly with the experimental Tg to estimate the accuracy of the present model. Using the full dataset of 1321 polymers, a new ML model for Tg was built following past work. The RMSE of prediction for the extended dataset, when compared to the earlier one, decreased to 27 K from 57 K. To further improve the performance of the Tg prediction model, we are continuing to accumulate new data and exploring new ML approaches.

physics.comp-ph↗