9 research outputs found
Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy
Accurate classification of molecular chemical motifs from experimental
measurement is an important problem in molecular physics, chemistry and
biology. In this work, we present neural network ensemble classifiers for
predicting the presence (or lack thereof) of 41 different chemical motifs on
small molecules from simulated C, N and O K-edge X-ray absorption near-edge
structure (XANES) spectra. Our classifiers not only reach a maximum average
class-balanced accuracy of 0.99 but also accurately quantify uncertainty. We
also show that including multiple XANES modalities improves predictions notably
on average, demonstrating a "multi-modal advantage" over any single modality.
In addition to structure refinement, our approach can be generalized for broad
applications with molecular design pipelines
Crystallography companion agent for high-throughput materials discovery
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 measurements, greatly outpacing XRD analysis techniques that
remain manual, time-consuming, error-prone, and impossible to scale. With the
advent of autonomous robotic scientists or self-driving labs, contemporary
techniques prohibit the integration of XRD. Here, we describe a computer
program for the autonomous characterization of XRD data, driven by artificial
intelligence (AI), for the discovery of new materials. Starting from structural
databases, we train an ensemble model using a physically accurate synthetic
dataset, which output probabilistic classifications -- rather than absolutes --
to overcome the overconfidence in traditional neural networks. This AI agent
behaves as a companion to the researcher, improving accuracy and offering
significant time savings. It was demonstrated on a diverse set of organic and
inorganic materials characterization challenges. This innovation is directly
applicable to inverse design approaches, robotic discovery systems, and can be
immediately considered for other forms of characterization such as spectroscopy
and the pair distribution function.Comment: For associated code, see https://github.com/maffettone/xc
Self-driving Multimodal Studies at User Facilities
Multimodal characterization is commonly required for understanding materials.
User facilities possess the infrastructure to perform these measurements,
albeit in serial over days to months. In this paper, we describe a unified
multimodal measurement of a single sample library at distant instruments,
driven by a concert of distributed agents that use analysis from each modality
to inform the direction of the other in real time. Powered by the Bluesky
project at the National Synchrotron Light Source II, this experiment is a
world's first for beamline science, and provides a blueprint for future
approaches to multimodal and multifidelity experiments at user facilities.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022). AI4Mat Worksho
What is missing in autonomous discovery: Open challenges for the community
Self-driving labs (SDLs) leverage combinations of artificial intelligence,
automation, and advanced computing to accelerate scientific discovery. The
promise of this field has given rise to a rich community of passionate
scientists, engineers, and social scientists, as evidenced by the development
of the Acceleration Consortium and recent Accelerate Conference. Despite its
strengths, this rapidly developing field presents numerous opportunities for
growth, challenges to overcome, and potential risks of which to remain aware.
This community perspective builds on a discourse instantiated during the first
Accelerate Conference, and looks to the future of self-driving labs with a
tempered optimism. Incorporating input from academia, government, and industry,
we briefly describe the current status of self-driving labs, then turn our
attention to barriers, opportunities, and a vision for what is possible. Our
field is delivering solutions in technology and infrastructure, artificial
intelligence and knowledge generation, and education and workforce development.
In the spirit of community, we intend for this work to foster discussion and
drive best practices as our field grows
Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X‑ray Absorption Spectroscopy
Accurate classification of molecular chemical motifs
from experimental
measurement is an important problem in molecular physics, chemistry,
and biology. In this work, we present neural network ensemble classifiers
for predicting the presence (or lack thereof) of 41 different chemical
motifs on small molecules from simulated C, N, and O K-edge X-ray
absorption near-edge structure (XANES) spectra. Our classifiers not
only achieve class-balanced accuracies of more than 0.95 but also
accurately quantify uncertainty. We also show that including multiple
XANES modalities improves predictions notably on average, demonstrating
a “multimodal advantage” over any single modality. In
addition to structure refinement, our approach can be generalized
to broad applications with molecular design pipelines
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
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 learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn't know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both "on-the-fly" and during analysis
What is missing in autonomous discovery:Open challenges for the community
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.</jats:p