An Ecosystem for Digital Reticular Chemistry

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

(2022) Yi Luo et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks

(2022) Daniele Ongari et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Chemistry-Encoded Convolutional Neural Networks for Predicting Gaseous Adsorption in Porous Materials

(2022) Ting-Hsiang Hung et al.   Journal of Physical Chemistry C

Approximating Continuous Functions on Persistence Diagrams Using Template Functions

(2022) Jose A. Perea et al.   FOUNDATIONS OF COMPUTATIONAL MATHEMATICS

Making the collective knowledge of chemistry open and machine actionable

(2022) Kevin Maik Jablonka et al.   Nature Chemistry

Ontologies4Chem: the landscape of ontologies in chemistry

(2022) Philip Strömert et al.   PURE AND APPLIED CHEMISTRY

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

(2022) Simon Axelrod et al.   Scientific Data

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

(2022) Aditya Nandy et al.   Scientific Data

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

(2022) Andrew S. Rosen et al.   npj Computational Materials

Evaluation guidelines for machine learning tools in the chemical sciences

(2022) Andreas Bender et al.   Nature Reviews Chemistry

A data-science approach to predict the heat capacity of nanoporous materials

(2022) Seyed Mohamad Moosavi et al.   NATURE MATERIALS

Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties

(2021) Eun Hyun Cho et al.   Journal of Physical Chemistry Letters

Topological representations of crystalline compounds for the machine-learning prediction of materials properties

(2021) Yi Jiang et al.   npj Computational Materials

Bias free multiobjective active learning for materials design and discovery

(2021) Kevin Maik Jablonka et al.   Nature Communications

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

(2021) Aditi S. Krishnapriyan et al.   Scientific Reports

Machine Learning Force Fields: Recent Advances and Remaining Challenges

(2021) Igor Poltavsky et al.   Journal of Physical Chemistry Letters

Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks

(2021) Kevin Maik Jablonka et al.   Nature Chemistry

Best practices in machine learning for chemistry

(2021) Nongnuch Artrith et al.   Nature Chemistry

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

(2021) Sauradeep Majumdar et al.   ACS Applied Materials & Interfaces

Building Unit Extractor for Metal–Organic Frameworks

(2021) Prosun Halder et al.   Journal of Chemical Information and Modeling

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

(2021) Aditya Nandy et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Average minimum distances of periodic point sets – foundational invariants for mapping periodic crystals

(2021) Daniel Widdowson et al.   MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY

Realizing the data-driven, computational discovery of metal-organic framework catalysts

(2021) Andrew S Rosen et al.   Current Opinion in Chemical Engineering

Inverse design of porous materials using artificial neural networks

(2020) Baekjun Kim et al.   Science Advances

Machine Learning for Materials Scientists: An introductory guide towards best practices

(2020) Anthony Yu-Tung Wang et al.   CHEMISTRY OF MATERIALS

Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials

(2020) Aditi S. Krishnapriyan et al.   Journal of Physical Chemistry C

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

(2020) Kevin Maik Jablonka et al.   CHEMICAL REVIEWS

Understanding the diversity of the metal-organic framework ecosystem

(2020) Seyed Mohamad Moosavi et al.   Nature Communications

Standard Practices of Reticular Chemistry

(2020) Cornelius Gropp et al.   ACS Central Science

A universal system for digitization and automatic execution of the chemical synthesis literature

(2020) S. Hessam M. Mehr et al.   SCIENCE

Materials Cloud, a platform for open computational science

(2020) Leopold Talirz et al.   Scientific Data

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

(2020) Sebastiaan P. Huber et al.   Scientific Data

Digital Reticular Chemistry

(2020) Hao Lyu et al.   Chem

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

(2020) Alexander Dunn et al.   npj Computational Materials

Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery

(2020) Liam Wilbraham et al.   ACCOUNTS OF CHEMICAL RESEARCH

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

(2020) Cameron J. Hargreaves et al.   CHEMISTRY OF MATERIALS

Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows

(2020) Martin Uhrin et al.   COMPUTATIONAL MATERIALS SCIENCE

GuacaMol: Benchmarking Models for de Novo Molecular Design

(2019) Nathan Brown et al.   Journal of Chemical Information and Modeling

Scaling tree-based automated machine learning to biomedical big data with a feature set selector

(2019) Trang T Le et al.   BIOINFORMATICS

Three pitfalls to avoid in machine learning

(2019) Patrick Riley   NATURE

Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis

(2019) Benjamin J. Bucior et al.   CRYSTAL GROWTH & DESIGN

Applicability of Tail Corrections in the Molecular Simulations of Porous Materials

(2019) Kevin Maik Jablonka et al.   Journal of Chemical Theory and Computation

Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent–Organic Frameworks

(2019) Daniele Ongari et al.   ACS Central Science

Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019

(2019) Yongchul G. Chung et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation

(2019) Zheng Xiong et al.   COMPUTATIONAL MATERIALS SCIENCE

Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

(2019) Peter G. Boyd et al.   NATURE

Matminer: An open source toolkit for materials data mining

(2018) Logan Ward et al.   COMPUTATIONAL MATERIALS SCIENCE

Distinguishing Metal–Organic Frameworks

(2018) Senja Barthel et al.   CRYSTAL GROWTH & DESIGN

High-throughput screening approach for nanoporous materials genome using topological data analysis: application to zeolites

(2018) Yongjin Lee et al.   Journal of Chemical Theory and Computation

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

(2018) Ryther Anderson et al.   CHEMISTRY OF MATERIALS

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Organic synthesis in a modular robotic system driven by a chemical programming language

(2018) Sebastian Steiner et al.   SCIENCE

50 Years of Data Science

(2017) David Donoho   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials

(2017) Daniele Ongari et al.   LANGMUIR

Quantifying similarity of pore-geometry in nanoporous materials

(2017) Yongjin Lee et al.   Nature Communications

A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory

(2016) Peter G. Boyd et al.   CRYSTENGCOMM

Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor

(2016) Jerome G. P. Wicker et al.   Journal of Chemical Information and Modeling

The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining

(2016) Henning Glawe et al.   NEW JOURNAL OF PHYSICS

The Cambridge Structural Database

(2016) Colin R. Groom et al.   Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials

What Are the Best Materials To Separate a Xenon/Krypton Mixture?

(2015) Cory M. Simon et al.   CHEMISTRY OF MATERIALS

User applications driven by the community contribution framework MPContribs in the Materials Project

(2015) P. Huck et al.   CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

(2015) David Dubbeldam et al.   MOLECULAR SIMULATION

Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals

(2014) Yongchul G. Chung et al.   CHEMISTRY OF MATERIALS

Evaluating different classes of porous materials for carbon capture

(2014) Johanna M. Huck et al.   Energy & Environmental Science

Bringing the MMFF force field to the RDKit: implementation and validation

(2014) Paolo Tosco et al.   Journal of Cheminformatics

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.

(2013) Robert P. Sheridan   Journal of Chemical Information and Modeling

Protein pocket and ligand shape comparison and its application in virtual screening

(2013) Matthias Wirth et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Characterization and comparison of pore landscapes in crystalline porous materials

(2013) Marielle Pinheiro et al.   JOURNAL OF MOLECULAR GRAPHICS & MODELLING

Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity

(2013) Michael Fernandez et al.   Journal of Physical Chemistry C

From rays to structures: Representation and selection of void structures in zeolites using stochastic methods

(2013) Andrew J. Jones et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

An Extended Charge Equilibration Method

(2012) Christopher E. Wilmer et al.   Journal of Physical Chemistry Letters

In silico screening of carbon-capture materials

(2012) Li-Chiang Lin et al.   NATURE MATERIALS

Is Science Mostly Driven by Ideas or by Tools?

(2012) F. J. Dyson   SCIENCE

Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

(2011) Thomas F. Willems et al.   MICROPOROUS AND MESOPOROUS MATERIALS

Crystallography Open Database – an open-access collection of crystal structures

(2009) Saulius Gražulis et al.   JOURNAL OF APPLIED CRYSTALLOGRAPHY

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

(2009) Lorenz C. Blum et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY