100 citations · 191 across the 11 of their papers we have counts for
13 papers
A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments
Adam A. Corrao, Phillip M. Maffettone, Bruce Ravel +5
High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, m…
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…
Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy
Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu +2
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neu…
Inverse Protein Folding Using Deep Bayesian Optimization
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson +5
Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of…
What is missing in autonomous discovery: Open challenges for the community
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…
Self-driving Multimodal Studies at User Facilities
Phillip M. Maffettone, Daniel B. Allan, Stuart I. Campbell +11
Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days t…
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver +4
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption for drug design has been hindered by the discrete, high-dimen…
Machine learning enabling high-throughput and remote operations at large-scale user facilities
Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel +3
Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques…
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
Lars Banko, Phillip M. Maffettone, Dennis Naujoks +2
We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations l…
Constrained non-negative matrix factorization enabling real-time insights of and high-throughput experiments
Phillip M. Maffettone, Aidan C. Daly, Daniel Olds
Non-negative Matrix Factorization (NMF) methods offer an appealing unsupervised learning method for real-time analysis of streaming spectral data in time-sensitive data collection,…
Crystallography companion agent for high-throughput materials discovery
Phillip M. Maffettone, Lars Banko, Peng Cui +5
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurem…
Negative Hydration Expansion in ZrW2O8: Microscopic Mechanism, Spaghetti Dynamics, and Negative Thermal Expansion
Mia Baise, Phillip M. Maffettone, Fabien Trousselet +3
We use a combination of X-ray diffraction, total scattering and quantum mechanical calculations to determine the mechanism responsible for hydration-driven contraction in ZrWO$…
Extreme cooperative swelling in topologically disordered fibre entanglements
Alistair R. Overy, Raj Pandya, Phillip M. Maffettone +3
Entangled states are ubiquitous amongst fibrous materials, whether naturally occurring (keratin, collagen, DNA) or synthetic (nanotube assemblies, elastane). A key mechanical chara…