292 citations · 372 across the 7 of their papers we have counts for
8 papers
Quantum Deep Equilibrium Models
Philipp Schleich, Marta Skreta, Lasse B. Kristensen +2
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth o…
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
Austin Cheng, Cher Tian Ser, Marta Skreta +8
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it i…
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang, Marta Skreta, Cher-Tian Ser +11
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutiona…
RePLan: Robotic Replanning with Perception and Language Models
Marta Skreta, Zihan Zhou, Jia Lin Yuan +3
Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have al…
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta +3
Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior d…
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization
Kourosh Darvish, Marta Skreta, Yuchi Zhao +9
Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure fa…
Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting
Marta Skreta, Naruki Yoshikawa, Sebastian Arellano-Rubach +6
Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in ge…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…