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

Abstract

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.

arXiv commentEditorial changes (typos fixed, minor adjustments to figures)

References in corpus (79)

• XGBoost: A Scalable Tree Boosting System
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Automatic chemical design using a data-driven continuous representation of molecules
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• On representing chemical environments
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• SchNet - a deep learning architecture for molecules and materials
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Molecular Graph Convolutions: Moving Beyond Fingerprints
• Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
• Reconciling modern machine learning practice and the bias-variance trade-off
• Predictive learning via rule ensembles
• A Review of Feature Selection Methods Based on Mutual Information
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• Deep Reinforcement Learning for De-Novo Drug Design
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• Big Data of Materials Science - Critical Role of the Descriptor
• A high-bias, low-variance introduction to Machine Learning for physicists
• Machine learning for molecular simulation
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• GuacaMol: Benchmarking Models for De Novo Molecular Design
• SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
• Comparing molecules and solids across structural and alchemical space
• Machine Learning Unifies the Modelling of Materials and Molecules
• Deep learning for molecular design - a review of the state of the art
• Active learning of linearly parametrized interatomic potentials
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• Nearsightedness of Electronic Matter
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• How to represent crystal structures for machine learning: towards fast prediction of electronic properties
• Advanced Steel Microstructural Classification by Deep Learning Methods
• Predicting Crystal Structures with Data Mining of Quantum Calculations
• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Insightful classification of crystal structures using deep learning
• Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Beyond scaling relations for the description of catalytic materials
• Compressive sensing as a new paradigm for model building
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• De novo exploration and self-guided learning of potential-energy surfaces
• Incorporating long-range physics in atomic-scale machine learning
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• On the Completeness of Atomic Structure Representations
• Pore-geometry recognition: on the importance of quantifying similarity in nanoporous materials
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Atom-Density Representations for Machine Learning
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Regression Conformal Prediction with Nearest Neighbours
• Learning with Known Operators reduces Maximum Training Error Bounds
• Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
• Learning physical descriptors for materials science by compressed sensing
• The jamming transition as a paradigm to understand the loss landscape of deep neural networks
• Neural Network-based Classification of Crystal Symmetries from X-Ray Diffraction Patterns
• Reliable Prediction Intervals with Regression Neural Networks
• Electron counting in solids: oxidation states, partial charges and ionicity
• Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
• Graph similarity drives zeolite diffusionless transformations and intergrowth
• Quantum Mechanics of Proteins in Explicit Water: The Role of Plasmon-Like Solute-Solvent Interactions
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
• Equation of state of fluid methane from first principles with machine learning potentials
• Recognizing Local and Global Structural Motifs at the Atomic Scale
• Gaussian Process-Based Refinement of Dispersion Corrections
• Learning phase transitions from dynamics
• Comment on "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning"
• Machine learning the computational cost of quantum chemistry
• A New Kind of Atlas of Zeolite Building Blocks
• Materials databases: the need for open, interoperable databases with standardized data and rich metadata
• Machine-learning of atomic-scale properties based on physical principles
• Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

Cited by in corpus (22)

• SELFIES and the future of molecular string representations
• 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
• Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
• High-throughput computational screening of nanoporous materials in targeted applications
• CRAFTED -- An exploratory database of simulated adsorption isotherms of metal-organic frameworks
• Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks
• Crystal structure prediction of materials with high symmetry using differential evolution
• MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
• Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat
• Parametrization of Non-Bonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach
• Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning
• MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
• Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions
• Accelerated calculation of configurational free energy using a combination of reverse Monte Carlo and neural network models: Adsorption isotherm for 2D square and triangular lattices
• A Hessian-Based Assessment of Atomic Forces for Training Machine Learning Interatomic Potentials
• Elastic heterogeneity governs asymmetric adsorption-desorption in a soft porous crystal
• Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice
• Coarse-grained crystal graph neural networks for reticular materials design
• Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality
• FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions
• TBHubbard: tight-binding and extended Hubbard model database for metal-organic frameworks
• Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints