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Researcher profile

Austin McDannald

Austin McDannald contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciMachine Learningphysics.app-phphysics.chem-phphysics.comp-ph

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Published work

6 published item(s)

preprint2026arXiv

Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials

Autonomous labs enable the integration of automated experiment execution, data analysis and decision making. The main challenge remains the integration of diverse data streams from multiple instruments, where the data is often heterogeneous and unsynchronized. The standard learning process of undetermined synthesis-process-structure-property relationships (SPSPR) usually relies on post-experiment analysis after data is fully collected, not during live experiments, and decision making is carried out independently across characterization equipment. Here, we demonstrate the Multi-instrument Autonomous Discovery (MAD) framework -- combining structural property mapping and functional property optimization simultaneously in a closed-loop manner. As an example, we applied MAD to phase change memory (PCM) materials, and, in particular on the Mn-Sb-Te ternary, a previously unexplored materials system for PCM. A multi-output model is employed to merge data from x-ray diffraction (XRD) and electrical resistance measurements simultaneously through a co-regionalization kernel that models the relationship between them. The output probabilistic posterior and uncertainty quantification facilitate decision making with shared knowledge, while the goals are different across tasks. We aimed to maximize the knowledge of crystal structure distribution using non-negative matrix factorization (NMF), while in parallel, we find the composition with the maximum resistance value, an important figure of merit for PCM. Leveraging MAD, we found promising electrical PCMs and identified the SPSPR within 25 closed-loop iterations, corresponding to a seven-fold speed-up. The framework opens a new path of study in large-scale autonomous facilities, where future experiments can be run in parallel together, not independently.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Intrinsic Direct Air Capture

We present new metrics to evaluate solid sorbent materials for Direct Air Capture (DAC). These new metrics provide a theoretical upper bound on CO2 captured per energy as well as a theoretical upper limit on the purity of the captured CO2. These new metrics are based entirely on intrinsic material properties and are therefore agnostic to the design of the DAC system. These metrics apply to any adsorption-refresh cycle design. In this work we demonstrate the use of these metrics with the example of temperature-pressure swing refresh cycles. The main requirement for applying these metrics is to describe the equilibrium uptake (along with a few other materials properties) of each species in terms of the thermodynamic variables (e.g. temperature, pressure). We derive these metrics from thermodynamic energy balances. To apply these metrics on a set of examples, we first generated approximations of the necessary materials properties for 11 660 metal-organic framework materials (MOFs). We find that the performance of the sorbents is highly dependent on the path through thermodynamic parameter space. These metrics allow for: 1) finding the optimum materials given a particular refresh cycle, and 2) finding the optimum refresh cycles given a particular sorbent. Applying these metrics to the database of MOFs lead to the following insights: 1) start cold - the equilibrium uptake of CO2 diverges from that of N2 at lower temperatures, and 2) selectivity of CO2 vs other gases at any one point in the cycle does not matter - what matters is the relative change in uptake along the cycle.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Benchmarking Active Learning Strategies for Materials Optimization and Discovery

Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active learning, the machine learning field of optimal experiment design, selects each subsequent experiment to maximize knowledge toward the user goal. Autonomous system performance can be further improved with implementation of scientific machine learning, also known as inductive bias-engineered artificial intelligence, which folds prior knowledge of physical laws (e.g., Gibbs phase rule) into the algorithm. As the number, diversity, and uses for active learning strategies grow, there is an associated growing necessity for real-world reference datasets to benchmark strategies. We present a reference dataset and demonstrate its use to benchmark active learning strategies in the form of various acquisition functions. Active learning strategies are used to rapidly identify materials with optimal physical properties within a ternary materials system. The data is from an actual Fe-Co-Ni thin-film library and includes previously acquired experimental data for materials compositions, X-ray diffraction patterns, and two functional properties of magnetic coercivity and the Kerr rotation. Popular active learning methods along with a recent scientific active learning method are benchmarked for their materials optimization performance. We discuss the relationship between algorithm performance, materials search space complexity, and the incorporation of prior knowledge.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system's composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO's autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO's search when used as a prior.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical, simultaneously addressing the most critical gaps in the inter-related space of sorbent material synthesis, processing, properties, and performance. The ability to collect these critical data streams in an agile, coordinated, and automated fashion will enable efficient end-to-end sorbent materials design through machine learning driven research framework.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties

The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow and there is a need to expedite such processes. We use Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption in metal organic frameworks (MOF), which are known for their high functional tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with 137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption isotherms. We develop high accuracy and fast models for pre-screening applications. We apply the trained model on CoREMOF database and computationally rank them for experimental synthesis. In addition to the CO2 adsorption isotherm, we also train models for electronic bandgaps, surface area, void fraction, lowest cavity diameter, and pore limiting diameter, and illustrate the strength and limitation of such graph neural network models. For a few candidate MOFs we carry out GCMC calculations to evaluate the deep-learning (DL) predictions.

0 citations0 reviewsNo code linkedOpen access