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Machine learning for heterogeneous catalyst design and discovery

AIChE journal, 2018-07, Vol.64 (7), p.2311-2323

2018

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New tolerance factor to predict the stability of perovskite oxides and halides

Science advances, 2019-02, Vol.5 (2), p.eaav0693-eaav0693

2019

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A map of the inorganic ternary metal nitrides

Nature materials, 2019-07, Vol.18 (7), p.732-739

2019

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Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Chemistry of materials, 2021-06, Vol.33 (11), p.4204-4215

2021

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Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry

Nature communications, 2018-10, Vol.9 (1), p.4168-10, Article 4168

2018

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A critical examination of compound stability predictions from machine-learned formation energies

npj computational materials, 2020-07, Vol.6 (1), Article 97

2020

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Synthetic accessibility and stability rules of NASICONs

Nature communications, 2021-10, Vol.12 (1), p.5752-5752, Article 5752

2021

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Review of computational approaches to predict the thermodynamic stability of inorganic solids

Journal of materials science, 2022-06, Vol.57 (23), p.10475-10498

Bartel, Christopher J.

2022

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Autonomous and dynamic precursor selection for solid-state materials synthesis

Nature communications, 2023-10, Vol.14 (1), p.6956-6956, Article 6956

2023

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Toward autonomous design and synthesis of novel inorganic materials

Materials horizons, 2021-08, Vol.8 (8), p.2169-2198

2021

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Operando X‑ray Diffraction Studies of the Mg-Ion Migration Mechanisms in Spinel Cathodes for Rechargeable Mg-Ion Batteries

Journal of the American Chemical Society, 2021-07, Vol.143 (28), p.10649-10658

2021

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Kinetically Controlled Low-Temperature Solid-State Metathesis of Manganese Nitride Mn3N2

Chemistry of materials, 2019-09, Vol.31 (18), p.7248-7254

2019

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Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteries

Chemical communications (Cambridge, England), 2020-02, Vol.56 (13), p.1952-1955

2020

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Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

npj computational materials, 2023-03, Vol.9 (1), p.31-8, Article 31

2023

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Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

Nanoscale, 2024-03, Vol.16 (13), p.6365-6382

2024

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Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

Chemistry of materials, 2022-08, Vol.34 (16), p.7323-7336

2022

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Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials

ACS central science, 2023-10, Vol.9 (10), p.1957-1975

2023

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Expanding the Ambient-Pressure Phase Space of CaFe2O4‑Type Sodium Postspinel Host–Guest Compounds

ACS Organic & Inorganic Au, 2022-02, Vol.2 (1), p.8-22

2022

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Inorganic Halide Double Perovskites with Optoelectronic Properties Modulated by Sublattice Mixing

Journal of the American Chemical Society, 2020-03, Vol.142 (11), p.5135-5145

2020

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Layered Transition Metal Oxides as Ca Intercalation Cathodes: A Systematic First‐Principles Evaluation

Advanced energy materials, 2021-12, Vol.11 (48), p.n/a

2021

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Data-centric approach to improve machine learning models for inorganic materials

Patterns (New York, N.Y.), 2021-11, Vol.2 (11), p.100382, Article 100382

Bartel, Christopher J.

2021

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An autonomous laboratory for the accelerated synthesis of novel materials

Nature (London), 2023-12, Vol.624 (7990), p.86-91

2023

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Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis

Advanced materials (Weinheim), 2021-06, Vol.33 (24), p.e2100312-n/a

2021

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Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

Chemistry of materials, 2024-01, Vol.36 (2), p.772-785

2024

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Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO 3

Chemistry of materials, 2024-01, Vol.36 (2), p.772-785

2024

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Machine learning for heterogeneous catalyst design and discovery

New tolerance factor to predict the stability of perovskite oxides and halides

A map of the inorganic ternary metal nitrides

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry

A critical examination of compound stability predictions from machine-learned formation energies

Synthetic accessibility and stability rules of NASICONs

Review of computational approaches to predict the thermodynamic stability of inorganic solids

Autonomous and dynamic precursor selection for solid-state materials synthesis

Toward autonomous design and synthesis of novel inorganic materials

Operando X‑ray Diffraction Studies of the Mg-Ion Migration Mechanisms in Spinel Cathodes for Rechargeable Mg-Ion Batteries

Kinetically Controlled Low-Temperature Solid-State Metathesis of Manganese Nitride Mn3N2

Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteries

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials

Expanding the Ambient-Pressure Phase Space of CaFe2O4‑Type Sodium Postspinel Host–Guest Compounds

Inorganic Halide Double Perovskites with Optoelectronic Properties Modulated by Sublattice Mixing

Layered Transition Metal Oxides as Ca Intercalation Cathodes: A Systematic First‐Principles Evaluation

Data-centric approach to improve machine learning models for inorganic materials

An autonomous laboratory for the accelerated synthesis of novel materials

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO 3

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