Remote and automated high-throughput powder diffraction measurements enabled by a robotic sample changer at SSRL beamline 2-1

The general-purpose powder diffractometer beamline (BL2-1) at the Stanford Synchrotron Radiation Lightsource (SSRL) is described. The evolution of design and performance of BL2-1 are presented, in addition to current operating specifications, applications and measurement capabilities. Recent developments involve a robotic sample changer enabling high-throughput X-ray diffraction measurements, applicable to mail-in and remote operations. In situ and operando capabilities to measure samples with different form factors (e.g. capillary, flat plate or thin film, and transmission) and under variable experimental conditions are discussed. Several example datasets and accompanying Rietveld refinements are presented.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

Utilization of machine learning to accelerate colloidal synthesis and discovery

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

Scrutinizing "Ligand Bands" via Polarized Single-Crystal X-ray Absorption Spectra of Copper(I) and Copper(II) Bis-2,2'-bipyridine Species

High-energy resolution fluorescence-detected Cu K-edge X-ray absorption spectroscopy (XAS) and single-crystal polarized XAS data are presented toward refining the assignments of bands assigned as excitations from Cu 1s to ligand-localized molecular orbitals. These have been previously dubbed "XAS-metal-ligand charge transfer" (XAS-MLCT) bands. Data are presented for a series of [Cu(xbpy)₂]ⁿ⁺ complexes (xbpy = 2,2'-bipyridine (1ⁿ⁺), 4,4'-bisamino-2,2'-bipyridine (2ⁿ⁺), and 4,4'-dimethoxy-2,2'-bipyridine (3ⁿ⁺); n = 1 and 2). Dipolar dependencies of these "XAS-MLCT" bands in both Cu¹⁺ and Cu²⁺ species lead to reassignment of these features as owing their intensities primarily to Cu 1s → Cu 4p excitations. The transition densities are Cu-localized, highlighting that XAS-MLCT features in Cu XAS spectra are not "charge transfer" transitions but rather quasi-atomic transitions. Although scrutiny of the acceptor orbitals supports assignment as Cu 1s → ligand π* transitions, it ultimately appears that while the ligand orbital energetics govern the positions of these bands the intensity is conferred through a small degree of metal 4p mixing into otherwise ligand-dominated acceptor molecular orbitals.

Electronic Structural Analysis of Copper(II)-TEMPO/ABNO Complexes Provides Evidence for Copper(I)-Oxoammonium Character

Copper/aminoxyl species are proposed as key intermediates in aerobic alcohol oxidation. Several possible electronic structural descriptions of these species are possible, and the present study probes this issue by examining four crystallographically characterized Cu/aminoxyl halide complexes by Cu K-edge, Cu L_2,3-edge, and Cl K-edge X-ray absorption spectroscopy. The mixing coefficients between Cu, aminoxyl, and halide orbitals are determined via these techniques with support from density functional theory. The emergent electronic structure picture reveals that Cu coordination confers appreciable oxoammonium character to the aminoxyl ligand. The computational methodology is extended to one of the putative intermediates invoked in catalytic Cu/aminoxyl-driven alcohol oxidation reactions, with similar findings. Collectively, the results have important implications for the mechanism of alcohol oxidation and the underlying basis for cooperativity in this co-catalyst system.

Probing Cu(I) in homogeneous catalysis using high-energy-resolution fluorescence-detected X-ray absorption spectroscopy

Metal-to-ligand charge transfer excitations in Cu(I) X-ray absorption spectra are introduced as spectroscopic handles for the characterization of species in homogeneous catalytic reaction mixtures. Analysis is supported by correlation of a spectral library to calculations and to complementary spectroscopic parameters.