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Understanding the diversity of the metal-organic framework ecosystem

Nature communications, 2020-08, Vol.11 (1), p.4068-4068, Article 4068

2020

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A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Chemical science (Cambridge), 2019, Vol.1 (34), p.7913-7922

2019

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical reviews, 2021-08, Vol.121 (16), p.9927-10000

2021

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Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Industrial & engineering chemistry research, 2018-10, Vol.57 (42), p.13973-13986

2018

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The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

Topics in catalysis, 2022-02, Vol.65 (1-4), p.296-311

2022

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MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks

Scientific data, 2022-03, Vol.9 (1), p.74-74, Article 74

2022

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Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations

Chemical science (Cambridge), 2017, Vol.8 (6), p.4399-4409

2017

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Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics

Physical chemistry chemical physics : PCCP, 2020-09, Vol.22 (34), p.19326-19341

2020

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Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Journal of the American Chemical Society, 2021-10, Vol.143 (42), p.17535-17547

2021

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Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

ACS catalysis, 2020-12, Vol.10 (24), p.15033-15047

2020

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Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning

The journal of physical chemistry. B, 2023-12, Vol.127 (49), p.10592-10600

2023

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Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Journal of chemical theory and computation, 2019-04, Vol.15 (4), p.2331-2345

2019

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Mechanistic Insights into Substrate Positioning That Distinguish Non-heme Fe(II)/α-Ketoglutarate-Dependent Halogenases and Hydroxylases

ACS catalysis, 2023-02, Vol.13 (4), p.2489-2501

2023

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Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Accounts of chemical research, 2021-02, Vol.54 (3), p.532-545

2021

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Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Inorganic chemistry, 2019-08, Vol.58 (16), p.10592-10606

2019

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The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

Topics in catalysis, 2021-07, Vol.65 (1-4)

2021

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Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

The journal of physical chemistry letters, 2021-05, Vol.12 (19), p.4628-4637

2021

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Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Current opinion in chemical engineering, 2022-06, Vol.36 (C), p.100778, Article 100778

2022

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Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

Journal of chemical theory and computation, 2020-07, Vol.16 (7), p.4373-4387

2020

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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

The journal of physical chemistry letters, 2020-08, Vol.11 (16), p.6640-6648

2020

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Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation

ACS catalysis, 2022-08, Vol.12 (15), p.9281-9306

2022

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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2020-04, Vol.124 (16), p.3286-3299

2020

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Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts

Journal of catalysis, 2024-03, Vol.431 (C), Article 115361

2024

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DFT-Based Multireference Diagnostics in the Solid State: Application to Metal–Organic Frameworks

Journal of chemical theory and computation, 2023-01, Vol.19 (1), p.190-197

2023

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Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify

Journal of computational chemistry, 2023-10, Vol.45 (6)

2023

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Understanding the diversity of the metal-organic framework ecosystem

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks

Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations

Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Mechanistic Insights into Substrate Positioning That Distinguish Non-heme Fe(II)/α-Ketoglutarate-Dependent Halogenases and Hydroxylases

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts

DFT-Based Multireference Diagnostics in the Solid State: Application to Metal–Organic Frameworks

Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify

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