4.6k citations · 31.4k across the 155 of their papers we have counts for
9 papers · 1 filter
Chemistry Lab Automation via Constrained Task and Motion Planning
Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish +6
Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process…
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
Manuel S. Drehwald, Sagi Eppel, Jolina Li +2
Visual recognition of materials and their states is essential for understanding most aspects of the world, from determining whether food is cooked, metal is rusted, or a chemical r…
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
R. A. Vargas-Hernández, K. Jorner, R. Pollice +1
Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model stil…
Group SELFIES: A Robust Fragment-Based Molecular String Representation
Austin Cheng, Andy Cai, Santiago Miret +3
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustne…
Machine Learning for a Sustainable Energy Future
Zhenpeng Yao, Yanwei Lum, Andrew Johnston +6
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient h…
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
AkshatKumar Nigam, Robert Pollice, Gary Tom +5
The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the mos…