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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2021

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

Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik

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Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials DiscoveryOriginalMachine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Trends in computational molecular catalyst design27Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States26Activating γ-graphyne nanoribbons as bifunctional electrocatalysts toward oxygen reduction and hydrogen evolution reactions by edge termination and nitrogen doping29Catalytic properties of the ferryl ion in the solid state: a computational review2Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost24Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond32An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML2Active meta-learning for predicting and selecting perovskite crystallization experiments24Tailor‐Engineered 2D Cocatalysts: Harnessing Electron–Hole Redox Center of 2D g‐C3N4 Photocatalysts toward Solar‐to‐Chemical Conversion and Environmental Purification181The long road to calibrated prediction uncertainty in computational chemistry33Ab Initio Methods in First‐Row Transition Metal Chemistry39Distributed representations of atoms and materials for machine learning41Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies21New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts74Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands18Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45A Theoretical Perspective on the Thermodynamic Stability of Polymer Blends for Solar Cells: From Experiments to Predictive Modeling16Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression12Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit36Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Performance of the r$^{2}$SCAN functional in transition metal oxides0Performance of the r2SCAN Functional in Transition Metal Oxides46Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence45Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces14Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules3Applications of metal nanoclusters supported on the two-dimensional material graphene in electrocatalytic carbon dioxide reduction14Deep Mind 21 functional does not extrapolate to transition metal chemistry25Transparency: The Missing Link to Boosting AI Transformations in Chemical Engineering21A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis27Challenges reconciling theory and experiments in the prediction of lattice thermal conductivity: the case of Cu-based sulvanites1Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites3A semi-automated quantum-mechanical workflow for the generation of molecular monolayers and aggregates4Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques10Machine Learning Mapping Approach for Computing Spin Relaxation Dynamics12DFT insights into the mechanical properties of NMs9A Machine Learning Model for the Prediction of Water Contact Angles on Solid Polymers24PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals4Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials1Machine learning prediction and efficient screening method for thermally induced fluorescence enhancement of Er 3+ doped materials6Leveraging Machine Learning for Thermoelectric Material Design: Addressing Composition–Property Relations and Data Imbalance Challenges2Data-driven and computational insights into borophene: Integrating machine learning with DFT and AI10A dual-descriptor machine learning framework for evaluating perovskite passivation materials0Informatics for thermodynamics: Stability to synthesizability0Machine learning-enhanced exploration of 2D MXene van der Waals heterostructures for energy storage6Promoting medium-chain carboxylic acids production in anaerobic digestion: Synergistic strategies of density functional theory and machine learning0Discerning the underlying trends in electrocatalytic hydrogen evolution activity of MOF/MOF-derived materials using data-driven approach4Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review13Machine learning for thermoelectric systems: Materials, design, and performance optimization: A review of approaches and recent trends1A Review on the Application of Machine Learning in Nitrogen Reduction Reaction0Large Language Model-Assisted Development of Reliable Machine Learning Models for Hydrogen Evolution Reaction Catalysts0

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  2. Trends in computational molecular catalyst design

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  4. Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States

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  7. Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

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  16. New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

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  17. Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks

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  18. Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

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  19. Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands

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