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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title What is a Minimal Working Example for a Self-driving Laboratory?
Author(s) Sterling G. Baird, Taylor D. Sparks
On-Site Speaker (Planned) Sterling G. Baird
Abstract Scope Self-driving labs are the future; however, the capital and expertise required can be daunting. We introduce an optimization task for less than $100, a square foot of desk space, and an hour of total setup time from the shopping cart to the first "autonomous drive." We use optics rather than chemistry for our demo. After all, light is easier to move than matter. While not materials-based, several core principles of a self-driving materials discovery lab are retained in this cross-domain example: sending commands to hardware to adjust physical parameters, receiving measured objective properties, decision-making via active learning, and utilizing cloud-based simulations. The demo is accessible, extensible, modular, and repeatable, making it an ideal candidate for low-cost prototyping of self-driving laboratory concepts and learning the principles of self-driving laboratories in a low-risk setting. Extensions to liquid- and solid-mixing demos will be discussed. Come find out how to set up your own miniature self-driving lab!
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Adaptive Discovery and Mixed-variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials
An Inverse Materials Design Route Based on Structure-property Linkages Leveraging 3D Convolutional Neural Network and Bayesian Optimization
Applying Data-driven Models in Materials Science: Unraveling Hidden Relationships between Structures and Properties
Atomistic Modeling of Electronic Transport and Electrochemistry
Band Gap Renormalization in 2D Materials from First-principles
Bridging First-principles Calculations with Experiment: Insights from Case Studies on (Photo)Electrochemical Systems
Closed Loop Computational Materials Discovery
Computation Discovery of Materials for Solid-state Batteries
Computational Design for Metallic Meso-architected Materials for Dynamics
Computer Vision Problems in Transmission Electron Microscopy
Crystal to PNG (xtal2png): A Screening Tool to Accelerate Domain Transfer from State-of-the-art Image-processing Models to Materials Informatics and a Case Study on Denoising Diffusion Probabilistic Models
Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials
Design and Development of High Strength High Conductivity Alloys using ICMDŽ Approach
Design of Bistable Metamaterials for Desired Dynamic Behavior
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
Designing Ohmic and Schottky Interfaces for Oxide Electronics
Developing an Ab Initio-Kinetic Passivation Model for High-throughput Screening of Material Stability
Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures
Elucidating the Mechanisms for Fast Diffusion in Doped LLZO
Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System
Graph Mining in Materials Science for the Prediction of Material Properties
M-16: Building an ImageNet for Materials Grain Boundaries
M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery
M-28: Molecular Dynamics Investigation of Electrochemical Systems
Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials
Machine Learning Assisted Discovery of Composite Solid-state Electrolytes in Context of Li-ion Batteries
Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys
Searching for New "Quantum Defects" through High-throughput Computational Screening
Ultra-fast Interpretable Machine-learning Potentials for Accelerated Structure Prediction of Materials
What is a Minimal Working Example for a Self-driving Laboratory?

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