These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments.
no code implementations • 15 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.
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.
no code implementations • 22 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.
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.
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)
no code implementations • 14 Jun 2022 • Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning.
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.
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse.
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.
Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions.
1 code implementation • 23 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.
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.
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.
1 code implementation • 15 May 2020 • Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
1 code implementation • 28 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.
2 code implementations • • 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.
3 code implementations • 20 Nov 2018 • Felipe Oviedo, Zekun Ren, Shijing Sun, Charlie Settens, Zhe Liu, Noor Titan Putri Hartono, Ramasamy Savitha, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.