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RESEARCH PAPER · Physical Chemistry Chemical Physics · 2020

Deep learning enabled inorganic material generator

Yashaswi Pathak, Karandeep Singh Juneja, Girish Varma, Masahiro Ehara, U. Deva Priyakumar

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Deep learning enabled inorganic material generatorOriginalDART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers13Generative models for inverse design of inorganic solid materials38MEMES: Machine learning framework for Enhanced MolEcular Screening39Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks52Enhanced Sampling of Chemical Space for High Throughput Screening Applications using Machine Learning3Enhanced Sampling of Chemical Space for High Throughput Screening Applications using Machine Learning2Big data and machine learning for materials science180LigGPT: Molecular Generation using a Transformer-Decoder Model13JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design17Crystal-Site-Based Artificial Neural Networks for Material Classification5Crystal Diffusion Variational Autoencoder for Periodic Material Generation97MolGPT: Molecular Generation Using a Transformer-Decoder Model543Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Artificial intelligence: machine learning for chemical sciences104Molecular representations for machine learning applications in chemistry93Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118Deep Convolutional Neural Network for Molecular EnergyPrediction0Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Assessing Deep Generative Models in Chemical Composition Space34A high-throughput computational dataset of halide perovskite alloys39DATA-DRIVEN INVERSE DESIGN OF RESONANT STRUCTURES FOR LOCALLY RESONANT METAMATERIALS4A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models1Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces1Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction14A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models10An evolutionary variational autoencoder for perovskite discovery11Deep Learning for Size‐Agnostic Inverse Design of Random‐Network 3D Printed Mechanical Metamaterials97Advances of machine learning in materials science: Ideas and techniques105Hitting stride by degrees: Fine grained molecular generation via diffusion model17Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness11Inverse design of semiconductor materials with deep generative models26The MatHub‐3d first‐principles repository and the applications on thermoelectrics37Methods and applications of machine learning in computational design of optoelectronic semiconductors22Vector Field Oriented Diffusion Model for Crystal Material Generation4Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders8A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning14Generative artificial intelligence for small molecule drug design32Machine Learning and Small Data-Guided Optimization of Silica Shell Morphology on Gold Nanorods10Artificial intelligence and machine learning at various stages and scales of process systems engineering36The Role of Autonomous Finance in the Era of Automatic Civilization0A brief overview of deep generative models and how they can be used to discover new electrode materials8Deep reinforcement learning for inverse inorganic materials design59A Latent Diffusion Model for Molecular Optimization0Machine Learning Algorithms for Applications in Materials Science I2Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches29Modern machine learning methods for protein property prediction10MGDM: Molecular generation using a multinomial diffusion model4Generative inverse design of multimodal resonant structures for locally resonant metamaterials7Deep Learning-Based Generation of Lead-Free Organic–Inorganic Hybrid Halide Perovskite Materials Using Conditional Variational Autoencoders0Octahedral-motif-guided design of optoelectronic semiconductors via interpretable machine learning0

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  1. DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers

    Physical Chemistry Chemical Physics · 2021-01-01

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  2. Generative models for inverse design of inorganic solid materials

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  3. MEMES: Machine learning framework for Enhanced MolEcular Screening

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    Sarvesh Mehta, Siddhartha Laghuvarapu, Yashaswi Pathak, Aaftaab Sethi, Mallika Alvala, U. Deva Priyakumar

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  4. Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks

    Journal of Chemical Information and Modeling · 2021-02-05

    Yashaswi Pathak, Sarvesh Mehta, U. Deva Priyakumar

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  5. Enhanced Sampling of Chemical Space for High Throughput Screening Applications using Machine Learning

    ChemRxiv · 2021-03-03

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  6. Enhanced Sampling of Chemical Space for High Throughput Screening Applications using Machine Learning

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    Sarvesh Mehta, Siddhartha Laghuvarapu, Yashaswi Pathak, Aaftaab Sethi, Mallika Alvala, U. Deva Priyakumar

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  7. Big data and machine learning for materials science

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    Jose Fernando Rodrigues Junior, Larisa Florea, Maria Cristina Ferreira de Oliveira, Dermot Diamond, Osvaldo N. Oliveira

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  8. LigGPT: Molecular Generation using a Transformer-Decoder Model

    ChemRxiv · 2021-05-11

    Viraj Bagal, Rishal Aggarwal, P. K. Vinod, U. Deva Priyakumar

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  9. JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

    arXiv (Cornell University) · 2021-06-07

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  10. Crystal-Site-Based Artificial Neural Networks for Material Classification

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    Juan Iván Gómez-Peralta, Nidia Guadalupe García-Peña, Xim Bokhimi

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  11. Crystal Diffusion Variational Autoencoder for Periodic Material Generation

    arXiv (Cornell University) · 2021-10-12

    Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola

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  12. MolGPT: Molecular Generation Using a Transformer-Decoder Model

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    Viraj Bagal, Rishal Aggarwal, Palakkad Krishnan Unni Vinod, U. Deva Priyakumar

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  13. Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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  14. Artificial intelligence: machine learning for chemical sciences

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  15. Molecular representations for machine learning applications in chemistry

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  16. Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

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  17. Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

    Advanced Materials Interfaces · 2022-01-30

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  18. Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

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  19. Deep Convolutional Neural Network for Molecular EnergyPrediction

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  20. Into the Unknown: How Computation Can Help Explore Uncharted Material Space

    Journal of the American Chemical Society · 2022-10-07

    Austin M. Mroz, Víctor Posligua, Andrew Tarzia, Emma Helen Wolpert, Kim E. Jelfs

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  21. Assessing Deep Generative Models in Chemical Composition Space

    Chemistry of Materials · 2022-10-19

    Hanna Türk, Elisabetta Landini, Christian Künkel, Johannes T. Margraf, Karsten Reuter

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  22. A high-throughput computational dataset of halide perovskite alloys

    Digital Discovery · 2023-01-01

    Jiaqi Yang, Panayotis Manganaris, Arun Mannodi‐Kanakkithodi

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  23. DATA-DRIVEN INVERSE DESIGN OF RESONANT STRUCTURES FOR LOCALLY RESONANT METAMATERIALS

    ECCOMAS Thematic Conference on Smart Structures and Materials (SMART) · 2023-01-01

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  24. A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models

    ChemRxiv · 2023-03-21

    Abhishek Tewari, Rochan Bajpai, Atharva Shukla, Janish Kumar

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  25. Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces

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  26. Convolutional Neural Networks to Assist the Assessment of Lattice Parameters from X-ray Powder Diffraction

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  28. An evolutionary variational autoencoder for perovskite discovery

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  29. Deep Learning for Size‐Agnostic Inverse Design of Random‐Network 3D Printed Mechanical Metamaterials

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  30. Advances of machine learning in materials science: Ideas and techniques

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  31. Hitting stride by degrees: Fine grained molecular generation via diffusion model

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  32. Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

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  36. Vector Field Oriented Diffusion Model for Crystal Material Generation

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