Machine-Learning-Assisted Synthesis of Polar Racemates

Matthew L. Nisbet; Ian M. Pendleton; Gene M. Nolis; Kent J. Griffith; Joshua Schrier; Jordi Cabana; Alexander J. Norquist; Kenneth R. Poeppelmeier

doi:10.1021/jacs.0c01239

Machine-Learning-Assisted Synthesis of Polar Racemates

Matthew L. Nisbet, Ian M. Pendleton, Gene M. Nolis, Kent J. Griffith, Joshua Schrier, Jordi Cabana, Alexander J. Norquist, Kenneth R. Poeppelmeier^*

^*Corresponding author for this work

Chemistry

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2′-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1¯), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal πbonding between Ti(3d-t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.

Original language	English (US)
Pages (from-to)	7555-7566
Number of pages	12
Journal	Journal of the American Chemical Society
Volume	142
Issue number	16
DOIs	https://doi.org/10.1021/jacs.0c01239
State	Published - Apr 22 2020

Funding

The authors would like to thank Professor Andrew Maverick for valuable discussions regarding speciation of copper-bipyridine complexes. This work was supported by funding from the National Science Foundation (DMR-1904701). Single-crystal and powder X-ray diffraction data and solid-state NMR spectra were acquired at IMSERC at Northwestern University, which has received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF ECCS-1542205), the State of Illinois, the International Institute for Nanotechnology (IIN), and the National Science Foundation (DMR-0521267). This work made use of the J. B. Cohen X-ray Diffraction Facility supported by the MRSEC program of the National Science Foundation (DMR-1720139). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Computational resources were provided by the MERCURY consortium ( http://mercuryconsortium.org/ ) under NSF grants CHE-1229354 and CHE-1662030. Contributions from A.J.N., I.M.P., and J.S. are supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001118C0036. J.S. acknowledges the Henry Dreyfus Teacher-Scholar Award (TH-14-010). K.J.G., G.M.N., and J.C. were supported as part of the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES).

ASJC Scopus subject areas

Catalysis
General Chemistry
Biochemistry
Colloid and Surface Chemistry

Access to Document

10.1021/jacs.0c01239

Cite this

@article{6d9be96581e54be09c9fb0179faae978,

title = "Machine-Learning-Assisted Synthesis of Polar Racemates",

abstract = "Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2′-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1¯), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal πbonding between Ti(3d-t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.",

author = "Nisbet, {Matthew L.} and Pendleton, {Ian M.} and Nolis, {Gene M.} and Griffith, {Kent J.} and Joshua Schrier and Jordi Cabana and Norquist, {Alexander J.} and Poeppelmeier, {Kenneth R.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2020 American Chemical Society.",

year = "2020",

month = apr,

day = "22",

doi = "10.1021/jacs.0c01239",

language = "English (US)",

volume = "142",

pages = "7555--7566",

journal = "Journal of the American Chemical Society",

issn = "0002-7863",

publisher = "American Chemical Society",

number = "16",

}

TY - JOUR

T1 - Machine-Learning-Assisted Synthesis of Polar Racemates

AU - Nisbet, Matthew L.

AU - Pendleton, Ian M.

AU - Nolis, Gene M.

AU - Griffith, Kent J.

AU - Schrier, Joshua

AU - Cabana, Jordi

AU - Norquist, Alexander J.

AU - Poeppelmeier, Kenneth R.

PY - 2020/4/22

Y1 - 2020/4/22

N2 - Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2′-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1¯), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal πbonding between Ti(3d-t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.

AB - Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2′-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1¯), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal πbonding between Ti(3d-t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.

UR - http://www.scopus.com/inward/record.url?scp=85087118530&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85087118530&partnerID=8YFLogxK

U2 - 10.1021/jacs.0c01239

DO - 10.1021/jacs.0c01239

M3 - Article

C2 - 32233475

AN - SCOPUS:85087118530

SN - 0002-7863

VL - 142

SP - 7555

EP - 7566

JO - Journal of the American Chemical Society

JF - Journal of the American Chemical Society

IS - 16

ER -

Machine-Learning-Assisted Synthesis of Polar Racemates

Abstract

Funding

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

CCDC 1972395: Experimental Crystal Structure Determination

CCDC 1972390: Experimental Crystal Structure Determination

CCDC 1972393: Experimental Crystal Structure Determination

Cite this

Machine-Learning-Assisted Synthesis of Polar Racemates

Abstract

Funding

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Datasets

CCDC 1972395: Experimental Crystal Structure Determination

CCDC 1972390: Experimental Crystal Structure Determination

CCDC 1972393: Experimental Crystal Structure Determination

Cite this