Predicting materials properties without crystal structure: Deep representation learning from stoichiometry

1 October 2019

Papers citing "Predicting materials properties without crystal structure: Deep representation learning from stoichiometry"

20 / 20 papers shown

Title
Learning simple heuristic rules for classifying materials based on chemical composition Andrew Ma Marin Soljacic 28 0 0 05 May 2025
Neural Network Emulator for Atmospheric Chemical ODE Zhi-Song Liu Petri S. Clusius Michael Boy 42 3 0 03 Aug 2024
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction Hongshuo Huang Rishikesh Magar Chang Xu A. Farimani AI4CE 38 4 0 30 Aug 2023
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions Janosh Riebesell Rhys E. A. Goodall Philipp Benner Chiang Yuan Bowen Deng A. Lee Anubhav Jain Kristin A. Persson OOD 36 35 0 28 Aug 2023
Leveraging Language Representation for Material Recommendation, Ranking, and Exploration Jiaxing Qu Yuxuan Richard Xie K. Ciesielski Claire E. Porter E. Toberer Elif Ertekin 29 3 0 01 May 2023
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials Hitarth Choubisa Md Azimul Haque Tong Zhu Lewei Zeng Maral Vafaie Derya Baran E. Sargent 17 1 0 26 Feb 2023
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature T. He Haoyan Huo Christopher J. Bartel Zheren Wang Kevin Cruse Gerbrand Ceder 32 32 0 05 Feb 2023
Neural Structure Fields with Application to Crystal Structure Autoencoders Naoya Chiba Yuta Suzuki Tatsunori Taniai Ryo Igarashi Yoshitaka Ushiku Kotaro Saito K. Ono 24 4 0 08 Dec 2022
Composition based oxidation state prediction of materials using deep learning Nihang Fu Jeffrey Hu Yingqi Feng G. Morrison H. Loye Jianjun Hu 43 1 0 29 Nov 2022
Deep Reinforcement Learning for Inverse Inorganic Materials Design Elton Pan Christopher Karpovich E. Olivetti AI4CE 27 11 0 21 Oct 2022
Large-scale machine-learning-assisted exploration of the whole materials space Jonathan Schmidt Noah Hoffmann Hai-Chen Wang Pedro Borlido Pedro J. M. A. Carriço Tiago F. T. Cerqueira S. Botti Miguel A. L. Marques AI4CE 8 17 0 02 Oct 2022
Periodic Graph Transformers for Crystal Material Property Prediction Keqiang Yan Yi Liu Yu-Ching Lin Shuiwang Ji AI4TS 88 84 0 23 Sep 2022
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering Lipichanda Goswami Manoj Deka Mohendra Roy AI4CE 37 19 0 15 Sep 2022
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 50 373 0 05 Aug 2022
Semi-supervised teacher-student deep neural network for materials discovery Daniel Gleaves Edirisuriya M Dilanga Siriwardane Yong Zhao Nihang Fu Jianjun Hu PINN 31 2 0 12 Dec 2021
Scalable deeper graph neural networks for high-performance materials property prediction Sadman Sadeed Omee Steph-Yves M. Louis Nihang Fu Lai Wei Sourin Dey Rongzhi Dong Qinyang Li Jianjun Hu 70 73 0 25 Sep 2021
Distributed Representations of Atoms and Materials for Machine Learning Luis M. Antunes R. Grau‐Crespo K. Butler AI4CE 8 26 0 30 Jul 2021
Geometric Deep Learning on Molecular Representations Kenneth Atz F. Grisoni G. Schneider AI4CE 34 287 0 26 Jul 2021
BoostTree and BoostForest for Ensemble Learning Changming Zhao Dongrui Wu Jian Huang Ye Yuan Hai-Tao Zhang Ruimin Peng Zhenhua Shi 32 33 0 21 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016