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Inorganic Materials Synthesis Planning with Literature-Trained Neural
  Networks

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

31 December 2018
Edward J. Kim
Z. Jensen
Alexander van Grootel
Kevin Huang
Matthew Staib
Sheshera Mysore
Haw-Shiuan Chang
Emma Strubell
Andrew McCallum
Stefanie Jegelka
E. Olivetti
ArXivPDFHTML

Papers citing "Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks"

10 / 10 papers shown
Title
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Thorben Prein
Elton Pan
Sami Haddouti
Marco Lorenz
Janik Jehkul
...
Cansu Moran
Menelaos Panagiotis Fotiadis
Artur P. Toshev
E. Olivetti
Jennifer L.M. Rupp
49
0
0
06 Feb 2025
Are LLMs Ready for Real-World Materials Discovery?
Are LLMs Ready for Real-World Materials Discovery?
Santiago Miret
N. M. A. Krishnan
43
27
0
07 Feb 2024
MatSci-NLP: Evaluating Scientific Language Models on Materials Science
  Language Tasks Using Text-to-Schema Modeling
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Yurun Song
Santiago Miret
Bang Liu
33
29
0
14 May 2023
Precursor recommendation for inorganic synthesis by machine learning
  materials similarity from scientific literature
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
Machine Learning for a Sustainable Energy Future
Machine Learning for a Sustainable Energy Future
Zhenpeng Yao
Yanwei Lum
Andrew K. Johnston
L. M. Mejia-Mendoza
Xiaoxia Zhou
Yonggang Wen
Alán Aspuru-Guzik
E. Sargent
Z. Seh
32
210
0
19 Oct 2022
Audacity of huge: overcoming challenges of data scarcity and data
  quality for machine learning in computational materials discovery
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Aditya Nandy
Chenru Duan
Heather J. Kulik
AI4CE
27
45
0
02 Nov 2021
MOFSimplify: Machine Learning Models with Extracted Stability Data of
  Three Thousand Metal-Organic Frameworks
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
Aditya Nandy
Gianmarco G. Terrones
N. Arunachalam
Chenru Duan
D. Kastner
Heather J. Kulik
AI4CE
20
29
0
16 Sep 2021
Using Machine Learning and Data Mining to Leverage Community Knowledge
  for the Engineering of Stable Metal-Organic Frameworks
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
Aditya Nandy
Chenru Duan
Heather J. Kulik
AI4CE
19
116
0
24 Jun 2021
On the Limits of Learning to Actively Learn Semantic Representations
On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek
Gabriel Stanovsky
Yichu Zhou
Vivek Srikumar
Jonathan Berant
OffRL
24
9
0
05 Oct 2019
The Materials Science Procedural Text Corpus: Annotating Materials
  Synthesis Procedures with Shallow Semantic Structures
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Sheshera Mysore
Z. Jensen
Edward J. Kim
Kevin Huang
Haw-Shiuan Chang
Emma Strubell
Jeffrey Flanigan
Andrew McCallum
E. Olivetti
19
95
0
16 May 2019
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