Experiment Automation

Cloud Laboratory Infrastructure

Carnegie Mellon University established the world’s first academic cloud laboratory in 2021 via partnership with Emerald Cloud Lab, offering remote access to over 130 instrument types. This infrastructure enables continuous robotic experimentation with automated data organization and built-in reproducibility tracking—transforming how chemistry research is conducted.

Key Advantages

• 24/7 Operation: Continuous experimentation without human supervision
• Automated Documentation: Complete experimental provenance and data organization
• Reproducibility: Exact protocol replication and version control
• Remote Access: Experimentation from anywhere with internet connectivity
• Scalability: Parallel execution of multiple experimental campaigns

Automated Quality Control

We developed machine learning frameworks for detecting anomalies in automated experiments, published in Digital Discovery. Our system identifies issues such as air bubble contamination and instrument drift that could otherwise corrupt experimental results, enabling real-time intervention without requiring human oversight.

ML-Based Anomaly Detection

• Real-time monitoring of experimental outputs
• Identification of instrument malfunction and contamination
• Automated alerts and intervention protocols
• Learning from historical experimental data
• Integration with laboratory information management systems

ML-Guided Materials Discovery

A 2021 publication in Journal of the American Chemical Society demonstrated automated polymer synthesis paired with machine learning screening. The approach explored less than 0.9% of 50,000 potential compositions while identifying copolymers outperforming existing 19F MRI contrast agents by up to 50%.

Active Learning Strategy

A foundational methodology using disagreement between an ensemble of ML potentials to identify chemical space regions where models struggle, achieving 20-fold efficiency improvements over brute-force screening. This approach enables:

• Targeted exploration of high-uncertainty regions
• Efficient use of experimental resources
• Rapid convergence to optimal formulations
• Systematic coverage of design space

Software Tools for Automation

TorchANI

PyTorch implementation of neural network potentials with 250+ citations, enabling seamless integration with molecular workflow tools. TorchANI provides the computational backbone for ML-guided experimental design by rapidly evaluating molecular properties.

AFLOW-ML

RESTful API providing machine learning property predictions for materials applications. This tool enables automated workflows to query predicted properties and make decisions about experimental priorities without human intervention.

Closed-Loop Discovery

Our research integrates:

1. Computational Design: ML models propose promising candidates
2. Automated Synthesis: Cloud labs execute synthesis protocols
3. Automated Characterization: Robotic analysis of products
4. Quality Control: ML-based validation of experimental results
5. Feedback Learning: Results inform next iteration of computational models

Vision: Human-Machine Collaboration

We aim toward chemistry research conducted through seamless human-machine collaboration:

• Humans: Define research objectives, interpret results, make strategic decisions
• Machines: Execute experiments, monitor quality, optimize conditions, manage data
• AI: Guide exploration, predict outcomes, identify anomalies, learn from results

This division of labor amplifies human creativity and scientific intuition while leveraging machine precision and tirelessness.

Applications

Polymer Discovery

Automated synthesis and characterization of polymer libraries for specific applications:

• Medical imaging contrast agents
• Drug delivery materials
• Electronic and photonic materials

Reaction Optimization

High-throughput optimization of reaction conditions:

• Catalyst screening
• Solvent and temperature optimization
• Reagent stoichiometry

Formulation Science

Systematic exploration of multi-component formulations:

• Pharmaceutical formulations
• Battery electrolytes
• Coating materials

Impact on Reproducibility

Automation fundamentally improves scientific reproducibility:

• Exact Protocols: Digital protocols eliminate ambiguity
• Complete Documentation: Automated recording of all conditions
• Version Control: Tracking of protocol evolution
• Data Integrity: Elimination of transcription errors
• Accessibility: Protocols can be shared and replicated globally

Future Directions

Emerging priorities in experiment automation:

• Integration of more diverse analytical techniques
• Autonomous troubleshooting and error recovery
• Natural language interfaces for experiment specification
• Cross-laboratory protocol sharing and replication
• Real-time collaboration between distributed researchers
• Integration with literature mining and knowledge bases

Collaborations

This work leverages:

• CMU’s Emerald Cloud Lab infrastructure
• Partnerships with instrument manufacturers
• Collaborations with experimentalists across chemistry disciplines
• Industrial partners in pharmaceuticals and materials