800 citations · 3.2k across the 56 of their papers we have counts for
57 papers
Synthesizing like a chemist: an iterative, feedback-driven loop for materials discovery
Fang Sheng, Steven B. Torrisi, Amanda Volk +4
Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loo…
How Molecular Generative Models Organize Molecular Identity
Raul Ortega-Ochoa, Tejs Vegge, Jens S. Bakander +3
Generative models for matter are often evaluated as samplers over output representations, and their latent spaces are commonly used as proxies for navigating chemical space. Much l…
Closed-Loop Robotic Manipulation of Transparent Substrates for Self-Driving Laboratories using Deep Learning Micro-Error Correction
Kelsey Fontenot, Anjali Gorti, Iva Goel +2
Self-driving laboratories (SDLs) have accelerated the throughput and automation capabilities for discovering and improving chemistries and materials. Although these SDLs have autom…
Kosmos: An AI Scientist for Autonomous Discovery
Ludovico Mitchener, Angela Yiu, Benjamin Chang +34
Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that…
A closed-loop AI framework for hypothesis-driven and interpretable materials design
Kangyu Ji, Tianran Liu, Fang Sheng +3
Scientific hypothesis generation is central to materials discovery, yet current approaches often emphasize either conceptual (idea-to-data) reasoning or data-driven (data-to-idea)…
Disentangling the Effects of Simultaneous Environmental Variables on Perovskite Synthesis and Device Performance via Interpretable Machine Learning
Tianran Liu, Nicky Evans, Kangyu Ji +12
Despite the rapid rise in perovskite solar cell efficiency, poor reproducibility remains a major barrier to commercialization. Film crystallization and device performance are highl…
Multimodal transformers with elemental priors for phase classification of X-ray diffraction spectra
Kangyu Ji, Fang Sheng, Tianran Liu +2
Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless ca…
Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
Imon Mia, Armi Tiihonen, Anna Ernst +4
Bayesian Optimization (BO) machine learning method is increasingly used to guide experimental optimization tasks in materials science. To emulate the large number of input variable…
A tomographic interpretation of structure-property relations for materials discovery
Raul Ortega-Ochoa, Alán Aspuru-Guzik, Tejs Vegge +1
Recent advancements in machine learning (ML) for materials have demonstrated that "simple" materials representations (e.g., the chemical formula alone without structural informatio…
A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties
Alexander E. Siemenn, Basita Das, Kangyu Ji +2
Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While…
Exploring material compositions for synthesis using oxidation states
Maung Thway, Andy Paul Chen, Haiwen Dai +9
Recent advances in machine learning techniques have made it possible to use high-throughput screening to identify novel materials with specific properties. However, the large numbe…
Long-Term Research & Design Strategies for Fusion Energy Materials
David Cohen-Tanugi, Myles G. Stapelberg, Michael P. Short +4
Fusion energy is at an important inflection point in its development: multiple government agencies and private companies are now planning fusion pilot plants to deliver electricity…
Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning
Alexander E. Siemenn, Tonio Buonassisi
Bayesian optimization (BO) suffers from long computing times when processing highly-dimensional or large data sets. These long computing times are a result of the Gaussian process…
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models
Sarah J. Zhang, Samuel Florin, Ariel N. Lee +12
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 Compu…
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
Alexander E. Siemenn, Eunice Aissi, Fang Sheng +4
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-p…
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Alexander E. Siemenn, Zekun Ren, Qianxiao Li +1
Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property op…
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj +13
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downs…
Introducing flexible perovskites to the IoT world using photovoltaic-powered wireless tags
Sai Nithin Reddy Kantareddy, Rahul Bhattacharya, Sanjay E. Sarma +6
Billions of everyday objects could become part of the Internet of Things (IoT) by augmentation with low-cost, long-range, maintenance-free wireless sensors. Radio Frequency Identif…
What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?
Siyu Isaac Parker Tian, Aron Walsh, Zekun Ren +2
Conventional wisdom of materials modelling stipulates that both chemical composition and crystal structure are integral in the prediction of physical properties. However, recent de…
Processing Induced Distinct Charge Carrier Dynamics of Bulky Organic Halide Treated Perovskites
Benjia Dak Dou, Dane W. deQuilettes, Madeleine Laitz +11
State-of-the-art metal halide perovskite-based photovoltaics often employ organic ammonium salts, AX, as a surface passivator, where A is a large organic cation and X is a halide.…
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi +1
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 successf…
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
Rishi E. Kumar, Armi Tiihonen, Shijing Sun +3
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse. In this perspective, we review practical challenges hi…
Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing
Zhe Liu, Nicholas Rolston, Austin C. Flick +4
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commer…
Accelerated automated screening of viscous graphene suspensions with various surfactants for optimal electrical conductivity
Daniil Bash, Frederick Hubert Chenardi, Zekun Ren +5
Functional composite thin films have a wide variety of applications in flexible and/or electronic devices, telecommunications and multifunctional emerging coatings. Rapid screening…