An autonomous laboratory for the accelerated synthesis of novel materials

Szymanski, Nathan J.; Rendy, Bernardus; Fei, Yuxing; Kumar, Rishi E.; He, Tanjin; Milsted, David; McDermott, Matthew J.; Gallant, Max; Cubuk, Ekin Dogus; Merchant, Amil; Kim, Haegyeom; Jain, Anubhav; Bartel, Christopher J.; Persson, Kristin; Zeng, Yan; Ceder, Gerbrand

doi:10.1038/s41586-023-06734-w

An autonomous laboratory for the accelerated synthesis of novel materials

Journal Article · Wed Nov 29 00:00:00 EST 2023 · Nature (London)

DOI:https://doi.org/10.1038/s41586-023-06734-w· OSTI ID:2281696

^[1]; ^[1]; Fei, Yuxing ^[1]; Kumar, Rishi E. ^[2]; ^[1]; ^[2]; McDermott, Matthew J. ^[1]; Gallant, Max ^[1]; ^[3]; ^[3]; ^[2]; ^[2]; ^[2]; Persson, Kristin ^[1]; ^[2]; ^[1]

Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Google DeepMind, London (United Kingdom)

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2281696

Journal Information:: Nature (London), Journal Name: Nature (London) Journal Issue: 7990 Vol. 624; ISSN 0028-0836

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (44)

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis Miura, Akira; Bartel, Christopher J.; Goto, Yosuke Advanced Materials https://doi.org/10.1002/adma.202100312	journal	May 2021
Synthesis of Metastable Inorganic Solids with Extended Structures Cordova, Dmitri Leo M.; Johnson, David C. ChemPhysChem, Vol. 21, Issue 13 https://doi.org/10.1002/cphc.202000199	journal	June 2020
The elastic constants and structure of the vitreous system Mo-P-O Bridge, B.; Patel, N. D. Journal of Materials Science, Vol. 21, Issue 4 https://doi.org/10.1007/BF00553251	journal	April 1986
Review of computational approaches to predict the thermodynamic stability of inorganic solids Bartel, Christopher J. Journal of Materials Science, Vol. 57, Issue 23 https://doi.org/10.1007/s10853-022-06915-4	journal	February 2022
The crystal structure of lanthanum manganate(iii), LaMnO3, at room temperature and at 1273 K under N2 Norby, P.; Andersen, I. G. Krogh; Andersen, E. Krogh Journal of Solid State Chemistry, Vol. 119, Issue 1 https://doi.org/10.1016/0022-4596(95)80028-N	journal	October 1995
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
The glass-forming ability explained from local structural differences by NMR between glasses and crystals in alkali metaphosphates Muñoz, Francisco; Sánchez-Muñoz, Luis Journal of Non-Crystalline Solids, Vol. 503-504 https://doi.org/10.1016/j.jnoncrysol.2018.09.026	journal	January 2019
A data fusion approach to optimize compositional stability of halide perovskites Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe Matter, Vol. 4, Issue 4 https://doi.org/10.1016/j.matt.2021.01.008	journal	April 2021
Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature He, Tanjin; Sun, Wenhao; Huo, Haoyan Chemistry of Materials, Vol. 32, Issue 18 https://doi.org/10.1021/acs.chemmater.0c02553	journal	August 2020
Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra Szymanski, Nathan J.; Bartel, Christopher J.; Zeng, Yan Chemistry of Materials, Vol. 33, Issue 11 https://doi.org/10.1021/acs.chemmater.1c01071	journal	May 2021
Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions Huo, Haoyan; Bartel, Christopher J.; He, Tanjin Chemistry of Materials, Vol. 34, Issue 16 https://doi.org/10.1021/acs.chemmater.2c01293	journal	August 2022
Interface Stability in Solid-State Batteries Richards, William D.; Miara, Lincoln J.; Wang, Yan Chemistry of Materials, Vol. 28, Issue 1 https://doi.org/10.1021/acs.chemmater.5b04082	journal	December 2015
Toward Reaction-by-Design: Achieving Kinetic Control of Solid State Chemistry with Metathesis Martinolich, Andrew J.; Neilson, James R. Chemistry of Materials, Vol. 29, Issue 2 https://doi.org/10.1021/acs.chemmater.6b04861	journal	January 2017
Role of Pairwise Reactions on the Synthesis of Li_0.3La_0.57TiO₃ and the Resulting Structure–Property Correlations Malkowski, Thomas F.; Sacci, Robert L.; McAuliffe, Rebecca D. Inorganic Chemistry, Vol. 60, Issue 19 https://doi.org/10.1021/acs.inorgchem.1c02136	journal	September 2021
Recent Developments on Molten Salt Synthesis of Inorganic Nanomaterials: A Review Gupta, Santosh K.; Mao, Yuanbing The Journal of Physical Chemistry C, Vol. 125, Issue 12 https://doi.org/10.1021/acs.jpcc.0c10981	journal	February 2021
Rational Solid-State Synthesis Routes for Inorganic Materials Aykol, Muratahan; Montoya, Joseph H.; Hummelshøj, Jens Journal of the American Chemical Society, Vol. 143, Issue 24 https://doi.org/10.1021/jacs.1c04888	journal	June 2021
Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space Todd, Paul K.; McDermott, Matthew J.; Rom, Christopher L. Journal of the American Chemical Society, Vol. 143, Issue 37 https://doi.org/10.1021/jacs.1c06229	journal	September 2021
Computational predictions of energy materials using density functional theory Jain, Anubhav; Shin, Yongwoo; Persson, Kristin A. Nature Reviews Materials, Vol. 1, Issue 1 https://doi.org/10.1038/natrevmats.2015.4	journal	January 2016
Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional Sun, Jianwei; Remsing, Richard C.; Zhang, Yubo Nature Chemistry, Vol. 8, Issue 9 https://doi.org/10.1038/nchem.2535	journal	June 2016
Autonomy in materials research: a case study in carbon nanotube growth Nikolaev, Pavel; Hooper, Daylond; Webber, Frederick npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.31	journal	October 2016
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry Bartel, Christopher J.; Millican, Samantha L.; Deml, Ann M. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06682-4	journal	October 2018
Autonomous and dynamic precursor selection for solid-state materials synthesis Szymanski, Nathan J.; Nevatia, Pragnay; Bartel, Christopher J. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-42329-9	journal	October 2023
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods Ludwig, Alfred npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0205-0	journal	July 2019
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics Ren, Zekun; Oviedo, Felipe; Thway, Maung npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0277-x	journal	January 2020
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification Szymanski, Nathan J.; Bartel, Christopher J.; Zeng, Yan npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-00984-y	journal	March 2023
Raman imaging of ferroelastically configurable Jahn–Teller domains in LaMnO3 Kim, Yong-Jin; Park, Heung-Sik; Yang, Chan-Ho npj Quantum Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41535-021-00361-2	journal	June 2021
An autonomous portable platform for universal chemical synthesis Manzano, J. Sebastián; Hou, Wenduan; Zalesskiy, Sergey S. Nature Chemistry, Vol. 14, Issue 11 https://doi.org/10.1038/s41557-022-01016-w	journal	October 2022
The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides Bianchini, Matteo; Wang, Jingyang; Clément, Raphaële J. Nature Materials, Vol. 19, Issue 10 https://doi.org/10.1038/s41563-020-0688-6	journal	May 2020
A mobile robotic chemist Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V. Nature, Vol. 583, Issue 7815 https://doi.org/10.1038/s41586-020-2442-2	journal	July 2020
Text-mined dataset of inorganic materials synthesis recipes Kononova, Olga; Huo, Haoyan; He, Tanjin Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0224-1	journal	October 2019
Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization Chang, Jorge; Nikolaev, Pavel; Carpena-Núñez, Jennifer Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-64397-3	journal	June 2020
Selective metathesis synthesis of MgCr ₂ S ₄ by control of thermodynamic driving forces Miura, Akira; Ito, Hiroaki; Bartel, Christopher J. Materials Horizons, Vol. 7, Issue 5 https://doi.org/10.1039/C9MH01999E	journal	January 2020
Non-stoichiometry, structural defects and properties of LaMnO3+δ with high δ values (0.11≤δ≤0.29) Alonso, J. A.; Martínez-lope, M. J.; Casais, M. T. Journal of Materials Chemistry, Vol. 7, Issue 10 https://doi.org/10.1039/a704088a	journal	January 1997
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
High-temperature behaviour of ammonium dihydrogen phosphate Pardo, A.; Romero, J.; Ortiz, E. Journal of Physics: Conference Series, Vol. 935 https://doi.org/10.1088/1742-6596/935/1/012050	journal	December 2017
GSAS-II : the genesis of a modern open-source all purpose crystallography software package Toby, Brian H.; Von Dreele, Robert B. Journal of Applied Crystallography, Vol. 46, Issue 2 https://doi.org/10.1107/S0021889813003531	journal	March 2013
A scripting interface for GSAS-II O'Donnell, Jackson H.; Von Dreele, Robert B.; Chan, Maria K. Y. Journal of Applied Crystallography, Vol. 51, Issue 4 https://doi.org/10.1107/S1600576718008075	journal	July 2018
Thermodynamic limit for synthesis of metastable inorganic materials Aykol, Muratahan; Dwaraknath, Shyam S.; Sun, Wenhao Science Advances, Vol. 4, Issue 4 https://doi.org/10.1126/sciadv.aaq0148	journal	April 2018
Self-driving laboratory for accelerated discovery of thin-film materials MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz8867	journal	May 2020
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature He, Tanjin; Huo, Haoyan; Bartel, Christopher J. Science Advances, Vol. 9, Issue 23 https://doi.org/10.1126/sciadv.adg8180	journal	June 2023
Synthesis of many different types of organic small molecules using one automated process Li, J.; Ballmer, S. G.; Gillis, E. P. Science, Vol. 347, Issue 6227 https://doi.org/10.1126/science.aaa5414	journal	March 2015
Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals Kitson, Philip J.; Marie, Guillaume; Francoia, Jean-Patrick Science, Vol. 359, Issue 6373 https://doi.org/10.1126/science.aao3466	journal	January 2018
A robotic platform for flow synthesis of organic compounds informed by AI planning Coley, Connor W.; Thomas, Dale A.; Lummiss, Justin A. M. Science, Vol. 365, Issue 6453 https://doi.org/10.1126/science.aax1566	journal	August 2019
Handbook of Inorganic Substances 2017 Villars, Pierre; Cenzual, Karin; Gladyshevskii, Roman https://doi.org/10.1515/9783110445404	book	July 2017

Similar Records

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Journal Article · Thu Nov 10 19:00:00 EST 2022 · Progress in Materials Science · OSTI ID:2418248

Toward autonomous design and synthesis of novel inorganic materials

Journal Article · Tue May 18 20:00:00 EDT 2021 · Materials Horizons · OSTI ID:1828572

Related Subjects

36 MATERIALS SCIENCE
characterization and analytical techniques
computational methods
design
synthesis and processing

An autonomous laboratory for the accelerated synthesis of novel materials

Citation Formats

References (44)

Similar Records

Related Subjects