Search Results for author: Tonio Buonassisi

Found 18 papers, 8 papers with code

Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning

no code implementations8 Sep 2023 Alexander E. Siemenn, Tonio Buonassisi

These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments.

Bayesian Optimization

Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

no code implementations15 Jun 2023 Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, Iddo Drori

We curate a comprehensive dataset of 4, 550 questions and solutions from problem sets, midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and Computer Science (EECS) courses required for obtaining a degree.

Electrical Engineering Few-Shot Learning +3

Autocharacterization: Automated and Scalable Semiconductor Property Estimation from High-throughput Experiments using Computer Vision

no code implementations16 Mar 2023 Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, Tonio Buonassisi

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors.

Band Gap

Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark

no code implementations22 Nov 2022 Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera, Zad Chin, Keith Tyser, Gregory Hunter, Arvind Raghavan, Yann Hicke, Bryan A. Plummer, Ori Kerret, Tonio Buonassisi, Kate Saenko, Armando Solar-Lezama, Iddo Drori

For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly.

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)

1 code implementation26 Aug 2022 Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization.

Bayesian Optimization Disease Prediction +2

Introducing flexible perovskites to the IoT world using photovoltaic-powered wireless tags

no code implementations1 Jul 2022 Sai Nithin Reddy Kantareddy, Rahul Bhattacharya, Sanjay E. Sarma, Ian Mathews, Janak Thapa, Liu Zhe, Shijing Sun, Ian Marius Peters, Tonio Buonassisi

Our evaluation of the prototypes suggests that: i) flexible PV cells are durable up to a bending radius of 5 mm with only a 20 % drop in relative efficiency; ii) RFID communication range increased by 5x, and meets the energy needs (10-350 microwatt) to enable self-powered wireless sensors; iii) perovskite powered wireless sensors enable many battery-less sensing applications (e. g., perishable good monitoring, warehouse automation)

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

no code implementations1 Nov 2021 Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, Keith Butler

While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power.

BIG-bench Machine Learning Interpretable Machine Learning

Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing

1 code implementation1 Oct 2021 Zhe Liu, Nicholas Rolston, Austin C. Flick, Thomas W. Colburn, Zekun Ren, Reinhold H. Dauskardt, Tonio Buonassisi

With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18. 5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling.

Bayesian Optimization Benchmarking +1

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

1 code implementation23 May 2021 Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.

Active Learning Benchmarking +2

Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop

no code implementations6 May 2021 Alexander E. Siemenn, Matthew Beveridge, Tonio Buonassisi, Iddo Drori

Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials.

Bayesian Optimization

A robust low data solution: dimension prediction of semiconductor nanorods

no code implementations27 Oct 2020 Xiaoli Liu, Yang Xu, Jiali Li, Xuanwei Ong, Salwa Ali Ibrahim, Tonio Buonassisi, Xiaonan Wang

Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution.


Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization

1 code implementation28 Apr 2020 Felipe Oviedo, Zekun Ren, Xue Hansong, Siyu Isaac Parker Tian, Kaicheng Zhang, Mariya Layurova, Thomas Heumueller, Ning li, Erik Birgersson, Shijing Sun, Benji Mayurama, Ian Marius Peters, Christoph J. Brabec, John Fisher III, Tonio Buonassisi

Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost.

Applied Physics

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks

2 code implementations npj Computational Materials 2019 Felipe Oviedo, Zekun Ren, Shijing Sun, Charles Settens, Zhe Liu, Noor Titan Putri Hartono, Savitha Ramasamy, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi

We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.

BIG-bench Machine Learning Data Augmentation +6

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