Tonio Buonassisi

About

Tonio Buonassisi is a Professor of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). His research focuses on the application of artificial intelligence to develop new materials for societally beneficial applications, with particular emphasis on solar photovoltaics and technoeconomic analysis. His work has contributed to technology developments in numerous companies and has earned him several prestigious awards, including a US Presidential Early Career Award for Scientists and Engineers (PECASE), a National Science Foundation CAREER Award, and a Google Faculty Award. He directs the ADDEPT Center, a DOE-funded national center dedicated to making semi-transparent perovskite solar cells durable for terrestrial tandem applications. Additionally, he is the PI of the Accelerated Materials Lab for Sustainability (AMLS) at MIT and has served as the founding director of the Accelerated Materials Development for Manufacturing Programme in Singapore. Buonassisi is recognized for his dedication to education, evidenced by the MIT Everett Moore Baker Memorial Award for Excellence in Undergraduate Teaching and the widespread viewership of his OpenCourseware/YouTube lectures on photovoltaic technology.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Materials science
• Nanotechnology
• Chemistry
• Engineering
• Data science
• Political Science
• Management science

Selected publications

• Autonomous experimentation systems for materials development: A community perspective

  Matter · 2021 · 344 citations

  DOI

• Interpretable and Explainable Machine Learning for Materials Science and Chemistry

  Accounts of Materials Research · 2022 · 297 citations

  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. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling une…

  DOI

• Two-step machine learning enables optimized nanoparticle synthesis

  npj Computational Materials · 2021 · 227 citations

  Abstract In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spe…

  DOI

• Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

  npj Computational Materials · 2021 · 194 citations

  Senior authorCorresponding

  Abstract Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find…

  DOI

• How machine learning can help select capping layers to suppress perovskite degradation

  Nature Communications · 2020 · 169 citations

  and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

  DOI

Recent grants

• EPAS: Hierarchical Characterization of Optoelectronic Hyperdoped Silicon Devices for Terawatt-Scale Photovoltaics

  NSF · $370k · 2011–2015

• CAREER:Toward robust, scalable, and non-intermittent solar power: Silicon-based multijunction devices with integrated photocatalysis

  NSF · $400k · 2012–2017

• Collaborative Research: SusChEM: Air-stable, high-lifetime bismuth compounds as solar absorbers with perovskite-like band structures

  NSF · $300k · 2016–2020

Frequent coauthors

• Ian Marius Peters

  150 shared

• Barry Lai

  126 shared

• Riley E. Brandt

  Massachusetts Institute of Technology

  125 shared

• Zekun Ren

  124 shared

• Shijing Sun

  University of Washington

  115 shared

• Roy G. Gordon

  Harvard University

  108 shared

• Noor Titan Putri Hartono

  Helmholtz-Zentrum Berlin für Materialien und Energie

  105 shared

• Sin Cheng Siah

  Massachusetts Institute of Technology

  91 shared

Labs

• MIT Mechanical EngineeringPI

Awards & honors

• Presidential Early Career Award for Scientists and Engineers…
• NSF CAREER Award (2012)
• BOSCH Energy Research Network Award (2012)
• European Materials Research Society (E-MRS) Young Scientist…
• German Academic Exchange Service (DAAD) Graduate Research Fe…

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