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RESEARCH PAPER · Nature Communications · 2017

Learning from data to design functional materials without inversion symmetry

Prasanna V. Balachandran, Joshua Young, Turab Lookman, James M. Rondinelli

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Learning from data to design functional materials without inversion symmetryOriginalRecent Progress in Photocatalytic CO 2 Reduction Over Perovskite Oxides284Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns21Importance of Feature Selection in Machine Learning and Adaptive Design for Materials31Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory183Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning277Data-Mining Driven Design for Novel Perovskite-type Piezoceramics1On the nexus between atom probe microscopy and density functional theory simulations34Module-Guided Design Scheme for Deep-Ultraviolet Nonlinear Optical Materials195Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Artificial neural network enabled capacitance prediction for carbon-based supercapacitors137Hybrid Improper Ferroelectricity in (Sr,Ca) 3 Sn 2 O 7 and Beyond: Universal Relationship between Ferroelectric Transition Temperature and Tolerance Factor in n = 2 Ruddlesden–Popper Phases123Modeling and predicting responses of magnetoelectric materials2SrCdGeS4 and SrCdGeSe4: Promising Infrared Nonlinear Optical Materials with Congruent-Melting Behavior74Materials informatics: From the atomic-level to the continuum189Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design828From DFT to machine learning: recent approaches to materials science–a review848Machine-learning guided discovery of a new thermoelectric material126Module-Analysis-Assisted Design of Deep Ultraviolet Fluorooxoborates with Extremely Large Gap and High Structural Stability109Physics‐Guided Data‐Mining Driven Design of Room‐Temperature Multiferroic Perovskite Oxides5Machine learning guided design of functional materials with targeted properties60Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia77Coexistence of polar displacements and conduction in doped ferroelectrics: An ab initio comparative study20A -site cation size effect on oxygen octahedral rotations in acentric Ruddlesden-Popper alkali rare-earth titanates11Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning67Interface-induced magnetic polar metal phase in complex oxides48Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning23Representations and descriptors unifying the study of molecular and bulk systems17Bayesian Global Optimization applied to the design of shape-memory alloys6Ruddlesden–Popper perovskites in electrocatalysis330Efficient Phase Diagram Sampling by Active Learning67Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices508Machine Learning based prediction of noncentrosymmetric crystal materials30Machine-Learning-Optimized Aperiodic Superlattice Minimizes Coherent Phonon Heat Conduction152Big-Data Science in Porous Materials: Materials Genomics and Machine Learning597Adaptive machine learning for efficient materials design31A New Era of Inorganic Materials Discovery Powered by Data Science5Defect accommodation in off-stoichiometric (SrTiO3)nSrO Ruddlesden–Popper superlattices studied with positron annihilation spectroscopy13Structural Diversity in Layered Hybrid Perovskites, A2PbBr4 or AA′PbBr4, Templated by Small Disc-Shaped Amines39Mining the Correlations Between Optical Micrographs and Mechanical Properties of Cold-Rolled HSLA Steels Using Machine Learning Approaches26Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning98On the crystal chemistry of inorganic nitrides: crystal-chemical parameters, bonding behavior, and opportunities in the exploration of their compositional space17Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials18Hybrid improper ferroelectricity in A-site cation ordered Li2La2Ti3O10 ceramic with triple-layer Ruddlesden–Popper structure13Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit54Wood Modification Technologies132Structural Diversity of Ordered Pyrochlores82Magnetoelectric coupling dependent on ferroelectric switching paths in two-dimensional perovskite multiferroics8Structural Features in Some Layered Hybrid Copper Chloride Perovskites: ACuCl4 or A2CuCl427Advances in structural modification of perovskite semiconductors for visible light assisted photocatalytic CO2 reduction to renewable solar fuels: A review92Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple4Predicting energy and stability of known and hypothetical crystals using graph neural network43DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Coordination-Directed Structural Modulation and Design of Deep-Ultraviolet Nonlinear Optical Materials4DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions27Quantum machine learning for chemistry and physics151An overview of SrRuO3-based heterostructures for spintronic and topological phenomena37Polar Ferromagnet Induced by Fluorine Positioning in Isomeric Layered Copper Halide Perovskites20Polarity and Ferromagnetism in Two-Dimensional Hybrid Copper Perovskites with Chlorinated Aromatic Spacers48The Nexus between ASAT and Density Functional Theory0Identification of high-dielectric constant compounds from statistical design14Resampling Techniques for Materials Informatics: Limitations in Crystal Point Groups Classification11Catalytic Applications of Non‐Centrosymmetric Oxide Nanomaterials32Catalytic Applications of Non‐Centrosymmetric Oxide Nanomaterials15Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review86Explainable active learning in investigating structure–stability of SmFe12-α-βXαYβ structures X, Y {Mo, Zn, Co, Cu, Ti, Al, Ga}12The centre cannot hold0Data-driven materials discovery and synthesis using machine learning methods14Ground-state structures, electronic structure, transport properties and optical properties of Ca-based anti-Ruddlesden-Popper phase oxide perovskites8Catalytic heterostructured materials for CO 2 mitigation and conversion into fuels: a renewable energy approach towards a sustainable environment24Perovskite material for photocatalysis1Recent Advance of Machine Learning in Selecting New Materials7Search without Coverage: Exploration Blind Spots in AI-Guided Materials Discovery0The Problem of Epistemic Debt in Iterative Materials AI Pipelines0Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning33Strain generated by the stacking faults in epitaxial SrO(SrTiO 3 ) N Ruddlesden–Popper structures2Low-coordination transition metal sites on oxygen vacancy enriched strontium titanate-based perovskites enable highly selective photocatalytic CO2 reduction to CH451Designing ferromagnetic polar half-metals in short-period perovskite nickelates1Research progress on photocatalytic reduction of CO 2 based on ferroelectric materials21Machine learning for automated experimentation in scanning transmission electron microscopy112Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances77Extending the Family of Hybrid Layered Fluoride Perovskites2Structural, electronic, and magnetic properties of the possible pnictide superconductor BaFeAs 22Understanding secondary order parameters in perovskites with tilted octahedra5Machine learning prediction of materials properties from chemical composition: Status and prospects29First Principal calculations on measuring the internal electric field or inter-layer potential over B-type cation octahedra in Ruddlesden-popper (RP) type layered perovskites1Design and Theory of Switchable Linear Magnetoelectricity by Ferroelectricity in Type-I Multiferroics6Geometry-driven polar antiferromagnetic metallicity in a double-layered perovskite cobaltate9A review on comparative analysis of synergism in dry reforming of methane by using perovskite oxides in photo, thermal and photothermal catalysis2Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review16Crystal structures, dielectric properties, and thermal behavior in hybrid improper ferroelectric Li2Sm2Ti3O10 ceramics0Nonlinear Transverse Transport in a Ferromagnetic Polar Metal0Breaking symmetry yields a low-loss out-of-plane tunable microwave dielectric2

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