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Electrochemical Stability of Metastable Materials

Chemistry of materials, 2017-12, Vol.29 (23), p.10159-10167

2017

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Benchmarking the acceleration of materials discovery by sequential learning

Chemical science (Cambridge), 2020-03, Vol.11 (1), p.2696-276

2020

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Network analysis of synthesizable materials discovery

Nature communications, 2019-05, Vol.10 (1), p.2018-2018, Article 2018

2019

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Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

Proceedings of the National Academy of Sciences - PNAS, 2017-03, Vol.114 (12), p.3040-3043

2017

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Automated monitoring of electrocatalyst corrosion as a function of electrochemical history and electrolyte formulation

Chemical communications (Cambridge, England), 2024-01

2024

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Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

npj computational materials, 2020-07, Vol.6 (1), Article 109

2020

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Agents for sequential learning using multiple-fidelity data

Scientific reports, 2022-03, Vol.12 (1), p.4694-13, Article 4694

2022

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New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research

Energy & environmental science, 2022-09, Vol.15 (9), p.3775-3794

2022

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Machine learning-accelerated design and synthesis of polyelemental heterostructures

Science advances, 2021-12, Vol.7 (52), p.eabj5505-eabj5505

2021

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Stability and self-passivation of copper vanadate photoanodes under chemical, electrochemical, and photoelectrochemical operation

Physical chemistry chemical physics : PCCP, 2016-04, Vol.18 (14), p.9349-9352

2016

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Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

ACS combinatorial science, 2015-04, Vol.17 (4), p.224-233

2015

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High-Throughput Bubble Screening Method for Combinatorial Discovery of Electrocatalysts for Water Splitting

ACS combinatorial science, 2014-02, Vol.16 (2), p.47-52

2014

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Calibration of reconstruction parameters in atom probe tomography using a single crystallographic orientation

Ultramicroscopy, 2013-09, Vol.132, p.136-142

2013

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The Materials Provenance Store

Scientific data, 2023-04, Vol.10 (1), p.184-184, Article 184

2023

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Tracking materials science data lineage to manage millions of materials experiments and analyses

npj computational materials, 2019-07, Vol.5 (1), p.1-9, Article 79

2019

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Statistical Analysis and Interpolation of Compositional Data in Materials Science

ACS combinatorial science, 2015-02, Vol.17 (2), p.130-136

2015

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The materials experiment knowledge graph

Digital discovery, 2023-08, Vol.2 (4), p.99-914

2023

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Discovery and Characterization of a Pourbaix-Stable, 1.8 eV Direct Gap Bismuth Manganate Photoanode

Chemistry of materials, 2017-12, Vol.29 (23), p.10027-10036

2017

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Event-driven data management with cloud computing for extensible materials acceleration platforms

Digital discovery, 2024-02, Vol.3 (2), p.238-242

2024

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Combinatorial Discovery of Lanthanum–Tantalum Oxynitride Solar Light Absorbers with Dilute Nitrogen for Solar Fuel Applications

ACS combinatorial science, 2018-01, Vol.20 (1), p.26-34

2018

Volltextzugriff (PDF)

Data mining for isotope discrimination in atom probe tomography

Ultramicroscopy, 2013-09, Vol.132, p.121-128

2013

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High Throughput Light Absorber Discovery, Part 2: Establishing Structure–Band Gap Energy Relationships

ACS combinatorial science, 2016-11, Vol.18 (11), p.682-688

2016

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Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes

Chemical communications (Cambridge, England), 2018, Vol.54 (36), p.4625-4628

2018

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Phase‐Mapper: Accelerating Materials Discovery with AI

The AI magazine, 2018-03, Vol.39 (1), p.15-26

2018

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CRYSTAL: a multi-agent AI system for automated mapping of materials' crystal structures

MRS communications, 2019-06, Vol.9 (2), p.600-608

2019

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Electrochemical Stability of Metastable Materials

Benchmarking the acceleration of materials discovery by sequential learning

Network analysis of synthesizable materials discovery

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

Automated monitoring of electrocatalyst corrosion as a function of electrochemical history and electrolyte formulation

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

Agents for sequential learning using multiple-fidelity data

New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research

Machine learning-accelerated design and synthesis of polyelemental heterostructures

Stability and self-passivation of copper vanadate photoanodes under chemical, electrochemical, and photoelectrochemical operation

Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

High-Throughput Bubble Screening Method for Combinatorial Discovery of Electrocatalysts for Water Splitting

Calibration of reconstruction parameters in atom probe tomography using a single crystallographic orientation

The Materials Provenance Store

Tracking materials science data lineage to manage millions of materials experiments and analyses

Statistical Analysis and Interpolation of Compositional Data in Materials Science

The materials experiment knowledge graph

Discovery and Characterization of a Pourbaix-Stable, 1.8 eV Direct Gap Bismuth Manganate Photoanode

Event-driven data management with cloud computing for extensible materials acceleration platforms

Combinatorial Discovery of Lanthanum–Tantalum Oxynitride Solar Light Absorbers with Dilute Nitrogen for Solar Fuel Applications

Data mining for isotope discrimination in atom probe tomography

High Throughput Light Absorber Discovery, Part 2: Establishing Structure–Band Gap Energy Relationships

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes

Phase‐Mapper: Accelerating Materials Discovery with AI

CRYSTAL: a multi-agent AI system for automated mapping of materials' crystal structures

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