Chemistry Lab Automation via Constrained Task and Motion Planning

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, we propose a robot framework for manipulation that autonomously performs chemistry experiments. Our framework receives high-level abstract descriptions of chemistry experiments, perceives the lab workspace, and autonomously plans multi-step actions and motions. The robot interacts with a wide range of lab equipment and executes the generated plans. A key component of our method is constrained task and motion planning using PDDLStream solvers. Preventing collisions and spillage is done by introducing a constrained motion planner. Our planning framework can conduct different experiments employing implemented actions and lab tools. We demonstrate the utility of our framework on pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization.Comment: Equal author contribution from Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish, Yuchi Zhao and Haoping X

ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization

Chemistry experimentation is often resource- and labor-intensive. Despite the many benefits incurred by the integration of advanced and special-purpose lab equipment, many aspects of experimentation are still manually conducted by chemists, for example, polishing an electrode in electrochemistry experiments. Traditional lab automation infrastructure faces challenges when it comes to flexibly adapting to new chemistry experiments. To address this issue, we propose a human-friendly and flexible robotic system, ORGANA, that automates a diverse set of chemistry experiments. It is capable of interacting with chemists in the lab through natural language, using Large Language Models (LLMs). ORGANA keeps scientists informed by providing timely reports that incorporate statistical analyses. Additionally, it actively engages with users when necessary for disambiguation or troubleshooting. ORGANA can reason over user input to derive experiment goals, and plan long sequences of both high-level tasks and low-level robot actions while using feedback from the visual perception of the environment. It also supports scheduling and parallel execution for experiments that require resource allocation and coordination between multiple robots and experiment stations. We show that ORGANA successfully conducts a diverse set of chemistry experiments, including solubility assessment, pH measurement, recrystallization, and electrochemistry experiments. For the latter, we show that ORGANA robustly executes a long-horizon plan, comprising 19 steps executed in parallel, to characterize the electrochemical properties of quinone derivatives, a class of molecules used in rechargeable flow batteries. Our user study indicates that ORGANA significantly improves many aspects of user experience while reducing their physical workload. More details about ORGANA can be found at https://ac-rad.github.io/organa/

Augmented Lagrangian Method for Optimizing Non-Orthogonal Orbitals

We applied augmented Lagrangian method to optimize molecular wave function based on non-orthogonal orbitals (Spin coupled wave function; SCWF) for its grand-state energy. In contrast to the orthogonal-orbital-based electronic structure theory, SCWF includes spin eigenfunctions to satisfy the eigen states as the operator of the square of the spin. To obtain the ground-state energy of SCWF, therefore, it is necessary to optimize the orbital and the spin-coupling coefficients simultaneously. In this study, the spin-coupling and the orbital coefficients are optimized with the augmented Lagrangian method under the constrain of normality of the wave function.We employed this SCWF method to compute dissociative potential energy surfaces (PESs) of H2, H2-, He2+, and HLi. The obtained PESs by the SCWF method are close to these by full configuration interaction theory. These results indicate that the augmented Lagrangian method is effective to optimize SCWF.</p

Fast, Efficient Fragment-Based Coordinate Generation for Open Babel

Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task in cheminformatics and a range of molecular modeling. Fast, accurate, and open implementation of structure prediction is necessary for reproducible cheminformatics research. We introduce fragment-based coordinate generation for Open Babel, a widely accepted open source toolkit for cheminformatics. The new implementation significant improves speed and stereochemical accuracy, while retaining or improving accuracy of bond lengths, bond angles, and dihedral torsions. We first separate an input molecule into fragments by cutting at rotatable bonds. Coordinates of fragments are set according to the fragment library, which is prepared from open crystallographic databases. Since coordinates of multiple atoms are decided at once, coordinate prediction is accelerated over the previous rules-based implementation or the widely-used distance geometry methods in RDKit. This new implementation will be beneficial for a wide range of applications, including computational property prediction in polymers, molecular materials and drug design.</div

Sanitize It Yourself: Web-based molecular sanitization for machine-generated chemical structures

Many computer-aided drug design (CADD) methods using deep learning have recently been proposed to explore the chemical space toward novel scaffolds efficiently. However, there is a tradeoff between the ease of generating novel structures and the chemical feasibility of structural formulas. To overcome the limitations of computational filtering, we have implemented a web-based software in which users can share and evaluate computer-generated compounds. The web service is available at https://sanitizer.chemical.space/

Twitter Integration of Chemistry Software Tools

Social media activity on a research article is considered to be an altmetric, a new measure to estimate research impact. Demonstrating software on Twitter is a powerful way to attract attention from a larger audience. SNS integration of software can also lower the barriers to trying the tools and make it easier to save and share the output. We present three case studies of Twitter bots for cheminformatics: retrosynthetic analysis, 3D molecule viewer, and 2D chemical structure editor. These bots make software research more accessible to a broader range of people and facilitate the sharing of chemical knowledge, concepts, and ideas.</div

Digital pipette: Open hardware for liquid transfer in self-driving laboratories

Self-driving laboratories promise to democratize automated chemical laboratories. Accurate liquid handling is an essential operation in the context of chemical labs, and consequently a self-driving laboratory will require a robotic liquid handling and transfer. Although many pipettes are available for human scientists, robots cannot manipulate these pipettes due to the limitations of current robot gripper morphology. We propose an intuitive yet elegant design for a 3D-printed digital pipette designed for robots to carry out chemical experiments. Performance-evaluation experiments were carried out liquid transfer tasks. Our results show that robots with digital pipette could transfer liquids within 0.5% error. This error is comparable to the baseline set by commercially available human-handled pipettes

Does one need to polish electrodes in an eight pattern? Automation provides the answer.

Automation of electrochemical measurements can accelerate the discovery of new electroactive materials. One of the hurdles to automated electrochemical measurement is the pretreatment of electrodes because mechanical polishing is usually conducted manually. Here we investigate the automation of electrochemical measurements using a robotic arm. We demonstrate automated mechanical polishing using a polishing station with a moving polishing pad and evaluate the effect of different polishing motions. Our automatic polishing method improved the corroded electrodes, and we found the effect of polishing motions was not significant. This research is a step toward automating electrochemistry experiments without human intervention

From Eyes to Cameras: Computer Vision for High-Throughput Liquid-Liquid Separation

We present a modular, high-throughput (HT) automation platform for screening Liquid-Liquid Extraction (LLE) workup processes. Our automated hardware platform simultaneously screens up to 12 vials, and is coupled with a computer vision (CV) system for real-time monitoring of macroscopic visual cues. Our CV system, named HeinSight3.0, leverages machine learning and image analysis to classify and quantify multivariate visual cues such as liquid level, phase split clarity, turbidity, homogeneity, volume, and color. These cues, combined with process parameters like stir rate and temperature, enable real-time analysis of key workup processes (e.g., separation time, phase split quality, volume ratio of layers, color, and emulsion presence) to aid in the optimization of separation parameters. We demonstrate our system on three case-studies: impurity recovery, excess reagent removal, and Grignard workup. Our application of HeinSight3.0 on literature data also suggests high potential for generalizability and adaptability across different platforms and contexts. Overall, our work represents a significant step towards achieving end-to-end autonomous LLE screening guided by visual cues, contributing to the realization of a self-driving lab for workup processes