Machine Learning in Materials Science

연구주제 · Materials Chemistry

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현택환 · 이원보 · 정유성 · 박정원 · 남재욱 · 류재윤

논문 49

Self-driving laboratories in Korea: a new era of autonomous discovery · Cross-material catalyst discovery via deep learning · Spatiotemporal characterization of water diffusion anomalies in saline · Closed-loop optimization of nanoparticle synthesis enabled by robotics · Active learning with non- ab initio input features toward efficient CO · Structure-Based Synthesizability Prediction of Crystals Using Partiall · A Perspective on Foundation Models in Chemistry · Generative modelling of inorganic materials with explicit electronic s · Reliable Li-ion conductivity with efficient data generation and uncert · Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unifi · A structure translation model for crystal compounds · Heterogeneous Catalysis in Grammar School · Large Language Models for Inorganic Synthesis Predictions · Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theo · Materials discovery with extreme properties via reinforcement learning · Progress in Computational and Machine‐Learning Methods for Heterogeneo · Artificial Intelligence for Advanced Functional Materials: Progress an · Enhancing high-dimensional neural network potential accuracy in OLED s · Large-scale screening of high-entropy materials for superionic solid e · Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Ef · An invertible crystallographic representation for general inverse desi · Explainable Synthesizability Prediction of Inorganic Crystal Polymorph · Predicting synthesis recipes of inorganic crystal materials using elem · Enhancing Generalization in Synthesizability Prediction of Structurall · Machine-enabled inverse design of inorganic solid materials: promises · A local environment descriptor for machine-learned density functional · Practical Deep-Learning Representation for Fast Heterogeneous Catalyst · Microfluidic Screening‐Assisted Machine Learning to Investigate Vertic · Machine learning for renewable energy materials · Classifying Intermetallic Tetragonal Phase of All-d-Metal Heusler Allo · AI for Accelerated Materials Discovery: From Generative Design to Auto · Machine learning interatomic potentials for lithium battery electrolyt · Integrating reaction enumeration and machine learning for microkinetic · Recent progress in computational discovery of Heusler alloys · Accelerated chemical science with AI · An invertible crystallographic representation for general inverse desi · Predicting potentially hazardous chemical reactions using an explainab · Automated exploitation of the big configuration space of large adsorba · Evaluating global optimization and generative methods for low-energy a · Synthesis-Aware Materials Redesign via Large Language Models · Accelerated Structural Optimization for the Supported Metal System Bas · Synthesizability of materials stoichiometry using semi-supervised lear · Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐E · Inverse Design of Solid-State Materials via a Continuous Representatio · Perovskite synthesizability using graph neural networks · Active learning guides discovery of a champion four-metal perovskite o · Generative Adversarial Networks for Crystal Structure Prediction · Artificial neural network for the configuration problem in solids · Transformer-driven classification of soft condensed matter via referen

학문분야 1

Materials Chemistry