arXiv subjects

Paul Kienzle

Publications and source records attributed to Paul Kienzle.

3 recordsLinked to original sources

arXiv2026

Computed materials proposals depart from the structural memory of experimental discovery

Generative AI and high-throughput DFT pipelines propose millions of inorganic crystal structures, but lack a calibrated reference frame against experimentally realized chemistry. Here we embed 167,500 Inorganic Crystal Structure Database entries in a continuous structural-similarity space, partition it into graph communities, and replay them in time. Experimental discovery shows strong structural memory: 82.9% of new formulas enter pre-existing communities; new-community formation falls from 40.2% (1930s) to 2.6% (2010s). The communities are chemically meaningful, positively identifying nine textbook field-defining renaissances, including cuprates, colossal-magnetoresistance manganites, MAX phases, and Li-ion battery cathodes. Projecting GNoME, MatterGen-public, Materials Project, JARVIS-DFT, and Alexandria-PBE into frozen historical maps yields a cutoff-robust ordering: held-out ICSD > MatterGen > {GNoME ~ MP-theoretical} > JARVIS > Alexandria. Structural departure from experimental basins is not specific to generative AI but general across the tested computed sets. Combining structural proximity with reduced-formula precedent defines a historical synthesizability prior for triaging computed materials.

cond-mat.mtrl-sci↗

arXiv2026

Attention Is Not All You Need for Diffraction

Determining crystal symmetry from powder X-ray diffraction is a central problem in materials characterization, yet multiple space groups can produce indistinguishable patterns, making automated classification difficult. We show that attention-based architectures, while superior to convolutional networks for this task, are insufficient on their own: reliable symmetry extraction requires encoding crystallographic knowledge into both the network architecture and the training curriculum. We introduce a physics-informed transformer that classifies powder patterns into 99 extinction groups, the most specific symmetry classification accessible from diffraction data alone, using an explicit sin^2(theta) coordinate channel, physics-aware positional encoding, and a structured multi-task decoder that separates geometric rule learning from holistic pattern recognition. A three-stage curriculum of balanced synthetic pretraining, realistic fine-tuning with explicit preferred-orientation modeling, and Bayesian prior injection proves essential for bridging the synthetic-to-real domain gap, while post-hoc temperature scaling rather than additional training is the key remaining ingredient for robust real-data transfer. By mapping predictions onto the directed acyclic graph of maximal translationengleiche subgroups, we show that the calibrated model's errors are not random but physically structured: they remain local on the subgroup hierarchy and flow predominantly toward lower-symmetry descendants, consistent with the physical erasure of systematic-absence cues by real-world noise. These results establish that physics-informed target design, curriculum, and calibrated inference matter as much as model capacity for scientific machine learning on diffraction data.

cond-mat.mtrl-sci↗

arXiv2018

reductus: a stateless Python data-reduction service with a browser frontend

The online data reduction service reductus transforms measurements in experimental science from laboratory coordinates into physically meaningful quantities with accurate estimation of uncertainties based on instrumental settings and properties. This reduction process is based on a few well-known transformations, but flexibility in the application of the transforms and algorithms supports flexibility in experiment design, supporting a broader range of measurements than a rigid reduction scheme for data. The user interface allows easy construction of arbitrary pipelines from well-known data transforms using a visual dataflow diagram. Source data is drawn from a networked, open data repository. The Python backend uses intelligent caching to store intermediate results of calculations for a highly responsive user experience. The reference implementation allows immediate reduction of measurements as they are recorded for the three neutron reflectometry instruments at the NIST Center for Neutron Research (NCNR), without the need for visiting scientists to install additional software on their own computers.

physics.data-an↗