Papers citing Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
ACS symposium series · 2022-06-14
Jon Paul Janet
Read original paper ↗ ·Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis
Journal of Chemical Theory and Computation · 2022-06-23
Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Román‐Leshkov, Heather J. Kulik
Read original paper ↗ ·Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands
Journal of Chemical Theory and Computation · 2022-07-14
Chenru Duan, Adriana Ladera, Julian C. -L. Liu, Michael G. Taylor, Isuru Rangana Ariyarathna, Heather J. Kulik
Read original paper ↗ ·Machine learning: An overview
Elsevier eBooks · 2022-09-23
Eugen Hruška, Fang Liu
Read original paper ↗ ·Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
JACS Au · 2022-12-01
Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik
Read original paper ↗ ·A transferable recommender approach for selecting the best density functional approximations in chemical discovery
Nature Computational Science · 2022-12-22
Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik
Read original paper ↗ ·Deep learning metal complex properties with natural quantum graphs
Digital Discovery · 2023-01-01
Hannes Kneiding, Ruslan Yu. Lukin, Lucas Lang, Simen Sommerfelt Reine, Thomas Bondo Pedersen, Riccardo De Bin, David Balcells
Read original paper ↗ ·High-throughput computational workflow for ligand discovery in catalysis with the CSD
Catalysis Science & Technology · 2023-01-01
Marc A. S. Short, Clare A. Tovee, Charlotte E. Willans, Bao N. Nguyen
Read original paper ↗ ·Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
Physical Chemistry Chemical Physics · 2023-01-01
Yael Cytter, Aditya Nandy, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·Machine learning methods in photochemistry and photophysics
Elsevier eBooks · 2023-01-01
Jingbai Li, Morgane Vacher, Pavlo O. Dral, Steven A. Lopez
Read original paper ↗ ·Computational Discovery of Stable Metal–Organic Frameworks for Methane-to-Methanol Catalysis
Journal of the American Chemical Society · 2023-06-20
Husain Adamji, Aditya Nandy, Ilia Kevlishvili, Yuriy Román‐Leshkov, Heather J. Kulik
Read original paper ↗ ·Deep Learning-Based Modelling of Complex Photonic Crystal Slow Light Waveguides
IEEE Journal of Selected Topics in Quantum Electronics · 2023-08-15
Jianwei Chen, Ran Hao, Ibrahim Nasidi, Huangjie Zhang, Xilan Wang, Shangzhong Jin
Read original paper ↗ ·Stability Trends in disubstituted Cobaltocenium Based on the Analysis of the Machine Learning Models
The Journal of Physical Chemistry A · 2023-11-28
Shehani T. Wetthasinghe, Sophya V. Garashchuk, Vitaly A. Rassolov
Read original paper ↗ ·Artificial intelligence in catalysis
Elsevier eBooks · 2024-01-01
Srinivas Rangarajan
Read original paper ↗ ·Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning
ChemCatChem · 2024-01-05
S. Macé, Yingjian Xu, Bao N. Nguyen
Read original paper ↗ ·Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances
Science and Technology of Advanced Materials Methods · 2024-02-05
Khurram Shahzad, Andrei Ionut Mardare, Achim Walter Hassel
Read original paper ↗ ·Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts
Journal of Catalysis · 2024-02-09
Husain Adamji, Ilia Kevlishvili, Aditya Nandy, Yuriy Román‐Leshkov, Heather J. Kulik
Read original paper ↗ ·Reducing training data needs with minimal multilevel machine learning (M3L)
Machine Learning Science and Technology · 2024-05-13
Stefan Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel J. Arismendi‐Arrieta, Alastair J. A. Price, Surajit Nandi, Arghya Bhowmik, Kersti Hermansson, O. Anatole von Lilienfeld
Read original paper ↗ ·Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
Unknown journal · 2024-05-17
Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input
Journal of Computational Chemistry · 2024-06-08
Thijs Stuyver
Read original paper ↗ ·AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
Journal of Chemical Information and Modeling · 2024-11-24
Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, David Balcells
Read original paper ↗ ·Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Journal of Chemical Information and Modeling · 2024-11-28
Daniel B. K. Chu, David Alfredo Gonzalez-Narvaez, Ralf Meyer, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Many-body expansion based machine learning models for octahedral transition metal complexes
Machine Learning Science and Technology · 2024-12-01
Ralf Meyer, Daniel B. K. Chu, Heather J. Kulik
Read original paper ↗ ·Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
The Journal of Physical Chemistry Letters · 2024-12-16
Brennon L. Shanks, Harry W. Sullivan, Michael P. Hoepfner
Read original paper ↗ ·Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction
Journal of Catalysis · 2025-01-01
Haojun Jia, Chenru Duan, Gianmarco Terrones, Ilia Kevlishvili, Heather J. Kulik
Read original paper ↗ ·Machine learning for sustainable development in electronics
Elsevier eBooks · 2025-01-01
Iraklis Varlamis, Ilias E. Panagiotopoulos, Christos Chronis, George J. Dimitrakopoulos, Fayçal Bensaali
Read original paper ↗ ·Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds
Physical review. B./Physical review. B · 2025-02-04
Johann Lüder
Read original paper ↗ ·Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models
Journal of the American Chemical Society · 2025-07-03
Jieyu Lü, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia, Chenru Duan
Read original paper ↗ ·A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability
Industrial & Engineering Chemistry Research · 2025-07-14
Shengde Zhang, Zihao Wang, Hanyu Gao, Teng Zhou
Read original paper ↗ ·Representative Random Sampling of Chemical Space
Journal of Chemical Theory and Computation · 2025-12-02
Diego J. Monterrubio-Chanca, Guido Falk von Rudorff
Read original paper ↗ ·The role of atomic-level understanding in optimizing lithium titanate oxide based anodes for lithium-ion batteries
Materials Advances · 2025-12-03
Emaan Shahid, Abdul Majid
Read original paper ↗ ·Accelerating quasiparticle physics: a review on the synergy of machine learning and density functional theory for excitons, polaritons, and plasmons
Journal of Physics Condensed Matter · 2026-03-03
Hosein Alavi-Rad, Mahyar Hassani-Vasmejani, Meysam Bagheri Tagani
Read original paper ↗ ·A generic model for mixture adsorption property prediction (MAPP): Toward efficient discovery of metal-organic frameworks for CO2 capture
Separation and Purification Technology · 2026-03-09
S. Q. Zhang, Teng Zhou
Read original paper ↗ ·High-Throughput Discovery of Conformation-Switching Mechanophores with Enhanced Reactivity and Stability
Inorganic Chemistry · 2026-06-06
Xiao Huang, Roland G. St. Michel, Ilia Kevlishvili, Heather J. Kulik
Read original paper ↗ ·ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table
Journal of Chemical Information and Modeling · 2026-10-01
Jacob Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes Kästner, Heather J. Kulik
Read original paper ↗ ·