Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental science, from which we can develop new insights into materials synthesis for both robot and human chemists.

[1]  E. Kumacheva,et al.  The rise of self-driving labs in chemical and materials sciences , 2023, Nature Synthesis.

[2]  Christopher J. Bartel,et al.  Thermodynamic and Kinetic Barriers Limiting Solid-State Reactions Resolved through In Situ Synchrotron Studies of Lithium Halide Salts , 2023, Chemistry of Materials.

[3]  R. Z. Khaliullin,et al.  Combinatorial study of the Li-La-Zr-O system , 2022, Solid State Ionics.

[4]  F. Wudl,et al.  Chemical synthesis and materials discovery , 2022, Nature Synthesis.

[5]  Aine B. Connolly,et al.  Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams , 2021, Science advances.

[6]  Samuel J. Yang,et al.  Discovery of complex oxides via automated experiments and data science , 2021, Proceedings of the National Academy of Sciences.

[7]  Semion K. Saikin,et al.  Autonomous experimentation systems for materials development: A community perspective , 2021 .

[8]  K. Kovnir Predictive Synthesis , 2021, Chemistry of Materials.

[9]  Michelle Ting,et al.  Metastability in Li–La–Ti–O Perovskite Materials and Its Impact on Ionic Conductivity , 2021 .

[10]  Gerbrand Ceder,et al.  Toward autonomous design and synthesis of novel inorganic materials. , 2021, Materials horizons.

[11]  Christopher J. Bartel,et al.  A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra , 2021, Chemistry of Materials.

[12]  Joseph H. Montoya,et al.  Rational Solid-State Synthesis Routes for Inorganic Materials. , 2021, Journal of the American Chemical Society.

[13]  Christopher J. Bartel,et al.  Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis , 2020, Advanced materials.

[14]  Phillip M. Maffettone,et al.  Crystallography companion agent for high-throughput materials discovery , 2020, Nature Computational Science.

[15]  Kristin A. Persson,et al.  A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis , 2020, Nature Communications.

[16]  G. Ceder,et al.  Promises and Challenges of Next-Generation "Beyond Li-ion" Batteries for Electric Vehicles and Grid Decarbonization. , 2020, Chemical reviews.

[17]  Cormac Toher,et al.  On-the-fly closed-loop materials discovery via Bayesian active learning , 2020, Nature Communications.

[18]  Abel Fernandez,et al.  Searching for New Ferroelectric Materials Using High-Throughput Databases: An Experimental Perspective on BiAlO3 and BiInO3 , 2020, Chemistry of Materials.

[19]  Reiner Sebastian Sprick,et al.  A mobile robotic chemist , 2020, Nature.

[20]  G. Ceder,et al.  Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature , 2020, Chemistry of Materials.

[21]  G. Ceder,et al.  The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides , 2020, Nature Materials.

[22]  Johnathan E. Holladay,et al.  Basic Research Needs for Transformative Manufacturing , 2020 .

[23]  L. Archer,et al.  Designing solid-state electrolytes for safe, energy-dense batteries , 2020, Nature Reviews Materials.

[24]  A. Aspuru-Guzik,et al.  Self-driving laboratory for accelerated discovery of thin-film materials , 2019, Science Advances.

[25]  G. Ceder,et al.  Text-mined dataset of inorganic materials synthesis recipes , 2019, Scientific Data.

[26]  Sorelle A. Friedler,et al.  Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis , 2019, Nature.

[27]  G. Ceder,et al.  Non-equilibrium crystallization pathways of manganese oxides in aqueous solution , 2019, Nature Communications.

[28]  G. Ceder,et al.  Non-equilibrium crystallization pathways of manganese oxides in aqueous solution , 2019, Nature Communications.

[29]  Jakoah Brgoch,et al.  Understanding the blue-emitting orthoborate phosphor NaBaBO3:Ce3+ through experiment and computation , 2019, Journal of Materials Chemistry C.

[30]  John D. Perkins,et al.  An open experimental database for exploring inorganic materials , 2018, Scientific Data.

[31]  S. Ong,et al.  The thermodynamic scale of inorganic crystalline metastability , 2016, Science Advances.

[32]  J. Eckstein,et al.  Computational and experimental investigation for new transition metal selenides and sulfides: The importance of experimental verification for stability , 2016 .

[33]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[34]  Gerbrand Ceder,et al.  Interface Stability in Solid-State Batteries , 2016 .

[35]  Anne Strauss,et al.  Kinetics Of Materials , 2016 .

[36]  Nicola Doebelin,et al.  Profex: a graphical user interface for the Rietveld refinement program BGMN , 2015, Journal of applied crystallography.

[37]  Wei Chen,et al.  Nucleation of metastable aragonite CaCO3 in seawater , 2015, Proceedings of the National Academy of Sciences.

[38]  Anubhav Jain,et al.  The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles , 2015 .

[39]  Zelong Su,et al.  Kinetic Studies on the Synthesis of Monoclinic Li3V2(PO4)3 via Solid-State Reaction. , 2014, The journal of physical chemistry. A.

[40]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[41]  Anubhav Jain,et al.  Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis , 2012 .

[42]  Gerbrand Ceder,et al.  Synthesis, computed stability, and crystal structure of a new family of inorganic compounds: carbonophosphates. , 2012, Journal of the American Chemical Society.

[43]  Anubhav Jain,et al.  Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability , 2012 .

[44]  Anubhav Jain,et al.  Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations , 2011 .

[45]  Martin Jansen,et al.  A concept for synthesis planning in solid-state chemistry. , 2002, Angewandte Chemie.

[46]  A. Feltz,et al.  Structure and ionic conduction in solids. I: Na+-ion conducting glasses in the systems NaBSiO4-Na2SiO3, NaBSiO4-Na4SiO4 and NaBSiO4-Na3PO4 , 1987 .