Работы, на которые ссылается эта работа

Работ: 37

Работа: Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier +8
Статья2013Цитирований: 12
ABI
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
Muhammad Naveed Akhter, Saad Mekhilef, Hazlie Mokhlis +1
Статья2019Цитирований: 4
ABI
Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic A. Clark, Paul Czodrowski +8
Обзорная статья2019Цитирований: 4
ABI
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Takashi Toyao, Zen Maeno, Satoru Takakusagi +3
Статья2019Цитирований: 4
ABI
Predicting the state of charge and health of batteries using data-driven machine learning
Man‐Fai Ng, Jin Zhao, Qingyu Yan +2
Статья2020Цитирований: 4
ABI
Machine learning for alloys
Gus L. W. Hart, Tim Mueller, Cormac Toher +1
Обзорная статья2021Цитирований: 4
ABI
On representing chemical environments
Albert P. Bartók, Risi Kondor, Gábor Cśanyi
Статья2013Цитирований: 3
ABI
The atomic simulation environment—a Python library for working with atoms
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist +31
Статья2017Цитирований: 3
ABI
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie, Jeffrey C. Grossman
Статья2018Цитирований: 3
ABI
Machine-learning guided discovery of a new thermoelectric material
Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev +10
Статья2019Цитирований: 3
ABI
The rise of the X-ray atomic pair distribution function method: a series of fortunate events
Simon J. L. Billinge
Статья2019Цитирований: 3
ABI
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chi Chen, Weike Ye, Yunxing Zuo +2
Статья2019Цитирований: 3
ABI
Machine learning assisted materials design and discovery for rechargeable batteries
Yue Liu, Biru Guo, Xinxin Zou +2
Статья2020Цитирований: 3
ABI
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
Shuo Zhang, Yang Liu, Lei Xie
Препринт2020Цитирований: 3
ABI
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
Shufeng Kong, Francesco Ricci, Dan Guevarra +3
Статья2022Цитирований: 3
ABI
Unified representation of molecules and crystals for machine learning
Haoyan Huo, Matthias Rupp
Статья2022Цитирований: 3
ABI
Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review
Vaishali A. Sawant, Rashmi Deshmukh, Chetan J. Awati
Обзорная статья2022Цитирований: 3
ABI
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Aidan P. Thompson, Laura Swiler, Christian Robert Trott +2
Статья2014Цитирований: 2
ABI
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Jörg Behler, Michele Parrinello
Статья2007Цитирований: 2
ABI
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Oliver T. Unke, Markus Meuwly
Статья2019Цитирований: 2
ABI
Learning the electronic density of states in condensed matter
Chiheb Ben Mahmoud, Andrea Anelli, Gábor Cśanyi +1
Статья2020Цитирований: 2
ABI
Benchmarking graph neural networks for materials chemistry
Victor Fung, Jiaxin Zhang, Eric Juarez +1
Статья2021Цитирований: 2
ABI
Direct Prediction of Phonon Density of States With Euclidean Neural Networks.
Zhantao Chen, Nina Andrejevic, Tess Smidt +7
Статья2021Цитирований: 2
ABI
Machine learning potentials for extended systems: a perspective
Jörg Behler, Gábor Cśanyi
Статья2021Цитирований: 2
ABI
Без названия
ДругоеЦитирований: 2
ABI