Papers citing Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning
Journal of Chemical Information and Modeling · 2021
Jianing Lu, Song Xia, Jieyu Lü, Yingkai Zhang
Read original paper ↗ ·Machine learning in materials science: From explainable predictions to autonomous design
Computational Materials Science · 2021
Ghanshyam Pilania
Read original paper ↗ ·Transfer learning for solvation free energies: From quantum chemistry to experiments
Chemical Engineering Journal · 2021
Florence H. Vermeire, William H. Green
Read original paper ↗ ·Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence
Accounts of Chemical Research · 2021
Tetiana A. Zubatiuk, Olexandr Isayev
Read original paper ↗ ·Breaking the CCSD(T) Barrier for Machine Learned Potentials of Large Molecules: Demonstration for Acetylacetone
Venue not supplied by source · 2021
Chen Qu, Paul L. Houston, Riccardo Conte, Apurba Nandi, Joel M. Bowman
Read original paper ↗ ·Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout
Arthritis & Rheumatology · 2021
Xia Shen, Can Wang, Ningning Liang, Zhen Liu, Xinde Li, Zheng‐Jiang Zhu, Tony Raymond Merriman, Nicola Dalbeth, Robert A. Terkeltaub, Changgui Li, Huiyong Yin
Read original paper ↗ ·Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water
Catalysis Today · 2021
Manyi Yang, Luigi Bonati, Daniela Polino, Michele Parrinello
Read original paper ↗ ·Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
Frontiers in Chemistry · 2021
Johannes R. Loeffler, Monica L. Fernández‐Quintero, Franz Waibl, Patrick K. Quoika, Florian Hofer, Michael Schauperl, Klaus Roman Liedl
Read original paper ↗ ·AutoGraph: Autonomous Graph-Based Clustering of Small-Molecule Conformations
Journal of Chemical Information and Modeling · 2021
Kiyoto Aramis Tanemura, Susanta Das, Kenneth M. Merz
Read original paper ↗ ·Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison
The Journal of Physical Chemistry B · 2021
David Rosenberger, Justin S. Smith, Angel E. Garcı́a
Read original paper ↗ ·Machine learning builds full-QM precision protein force fields in seconds
Briefings in Bioinformatics · 2021
Yanqiang Han, Zhilong Wang, Zhiyun Wei, Jinyun Liu, Jinjin Li
Read original paper ↗ ·Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions
The Journal of Physical Chemistry Letters · 2021
Herim Han, Sunghwan Choi
Read original paper ↗ ·Deep learning for ultra-fast and high precision screening of energy materials
Energy storage materials · 2021
Zhilong Wang, Qingxun Wang, Yanqiang Han, Ma Yan, Hua Zhao, Andrzej P. Nowak, Jinjin Li
Read original paper ↗ ·Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials
Journal of Chemical Theory and Computation · 2021
Qidong Lin, Liang Zhang, Yaolong Zhang, Bin Jiang
Read original paper ↗ ·Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory
International Journal of Molecular Sciences · 2021
Dipankar Roy, Andriy Kovalenko
Read original paper ↗ ·Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
The Journal of Physical Chemistry Letters · 2021
Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Alchemical Transfer Approach to Absolute Binding Free Energy Estimation
Journal of Chemical Theory and Computation · 2021
Joe Z. Wu, Solmaz Azimi, Sheenam Khuttan, Nanjie Deng, Emilio Gallicchio
Read original paper ↗ ·ParAMS: Parameter Optimization for Atomistic and Molecular Simulations
Journal of Chemical Information and Modeling · 2021
Leonid Komissarov, Robert Rüger, Matti Hellström, Toon Verstraelen
Read original paper ↗ ·CLIFF: A component-based, machine-learned, intermolecular force field
The Journal of Chemical Physics · 2021
Jeffrey B. Schriber, Daniel R. Nascimento, Alexios Koutsoukas, Steven A. Spronk, Daniel L. Cheney, C. David Sherrill
Read original paper ↗ ·Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone
The Journal of Physical Chemistry Letters · 2021
Chen Qu, Paul L. Houston, Riccardo Conte, Apurba Nandi, Joel M. Bowman
Read original paper ↗ ·Molecular excited states through a machine learning lens
Nature Reviews Chemistry · 2021
Pavlo O. Dral, Mario Barbatti
Read original paper ↗ ·Highly Accurate Many-Body Potentials for Simulations of N 2 O 5 in Water: Benchmarks, Development, and Validation
Journal of Chemical Theory and Computation · 2021
Vinícius Wilian D. Cruzeiro, Eleftherios Lambros, Marc Riera, Ronak Roy, Francesco Paesani, Andreas W. Götz
Read original paper ↗ ·Machine-learned potentials for next-generation matter simulations
Nature Materials · 2021
Pascal Friederich, Florian Häse, Jonny Proppe, Alán Aspuru‐Guzik
Read original paper ↗ ·Best practices in machine learning for chemistry
Nature Chemistry · 2021
Nongnuch Artrith, Keith T. Butler, François‐Xavier Coudert, Seungwu Han, Olexandr Isayev, Anubhav Jain, Aron Walsh
Read original paper ↗ ·Federated Learning for Spanish Ports as an Aid to Digitization
Journal of Konbin · 2021
Nicoletta González–Cancelas, Beatriz Molina Serrano, Francisco Soler Flores
Read original paper ↗ ·Machine Learning for Chemical Reactions
Chemical Reviews · 2021
Markus Meuwly
Read original paper ↗ ·Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles
The Journal of Chemical Physics · 2021
Zachary L. Glick, Alexios Koutsoukas, Daniel L. Cheney, C. David Sherrill
Read original paper ↗ ·Real-time Prediction of 1H and 13C Chemical Shifts with DFT accuracy using a 3D Graph Neural Network
Venue not supplied by source · 2021
Yanfei Guan, Shree Sowndarya S. V., Liliana C. Gallegos, Peter C. St. John, Robert S. Paton
Read original paper ↗ ·Perspective on integrating machine learning into computational chemistry and materials science
The Journal of Chemical Physics · 2021
Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer
Read original paper ↗ ·Machine learned Hückel theory: Interfacing physics and deep neural networks
The Journal of Chemical Physics · 2021
Tetiana A. Zubatiuk, Benjamin T. Nebgen, Nicholas Lubbers, Justin S. Smith, R.I. Zubatyuk, Guoqing Zhou, Christopher Koh, Kipton M. Barros, Olexandr Isayev, Sergei Tretiak
Read original paper ↗ ·First principles reactive simulation for equation of state prediction
The Journal of Chemical Physics · 2021
Ryan B. Jadrich, Christopher Ticknor, Jeffery A. Leiding
Read original paper ↗ ·The Rise of Neural Networks for Materials and Chemical Dynamics
The Journal of Physical Chemistry Letters · 2021
Maksim Kulichenko, Justin S. Smith, Benjamin T. Nebgen, Ying Wai Li, Nikita S. Fedik, Alexander I. Boldyrev, Nicholas Lubbers, Kipton M. Barros, Sergei Tretiak
Read original paper ↗ ·Machine Learning Force Fields: Recent Advances and Remaining Challenges
The Journal of Physical Chemistry Letters · 2021
Igor Poltavsky, Alexandre Tkatchenko
Read original paper ↗ ·Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Chemical Reviews · 2021
Aditya Nandy, Chenru Duan, Michael G. Taylor, Fang Liu, Adam H. Steeves, Heather J. Kulik
Read original paper ↗ ·Learning molecular potentials with neural networks
Wiley Interdisciplinary Reviews Computational Molecular Science · 2021
Hatice Gökcan, Olexandr Isayev
Read original paper ↗ ·The Middle Science: Traversing Scale In Complex Many-Body Systems
ACS Central Science · 2021
Aurora E. Clark, Henry Adams, Rigoberto Hernandez, Anna I. Krylov, Anders M. N. Niklasson, Sapna Sarupria, Yusu Wang, Stefan M. Wild, Qian Yang
Read original paper ↗ ·Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions
Venue not supplied by source · 2021
Leif D. Jacobson, James Stevenson, Farhad Ramezanghorbani, Delaram Ghoreishi, Karl Leswing, Ed Harder, Robert Abel
Read original paper ↗ ·A Fragmentation-Based Graph Embedding Framework for QM/ML
The Journal of Physical Chemistry A · 2021
Eric M. Collins, Krishnan Raghavachari
Read original paper ↗ ·Teaching a neural network to attach and detach electrons from molecules
Nature Communications · 2021
R.I. Zubatyuk, Justin S. Smith, Benjamin T. Nebgen, Sergei Tretiak, Olexandr Isayev
Read original paper ↗ ·Ab Initio Machine Learning in Chemical Compound Space
Chemical Reviews · 2021
Bing Huang, O. Anatole von Lilienfeld
Read original paper ↗ ·Learning protein-ligand binding affinity with atomic environment vectors
Journal of Cheminformatics · 2021
Rocco Meli, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris, Philip Charles Biggin
Read original paper ↗ ·A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules
The Journal of Physical Chemistry Letters · 2021
Xufei Wang, Yuanda Xu, Han Zheng, Kuang Yu
Read original paper ↗ ·Gaussian Process Regression for Materials and Molecules
Chemical Reviews · 2021
Volker L. Deringer, Albert P. Bartók, Noam Bernstein, David M. Wilkins, Michele Ceriotti, Gábor Cśanyi
Read original paper ↗ ·Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
Venue not supplied by source · 2021
Pushkar G. Ghanekar, Siddharth Deshpande, Jeffrey Greeley
Read original paper ↗ ·Machine learning accelerates quantum mechanics predictions of molecular crystals
Physics Reports · 2021
Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu, Lei Hang, Hongyuan Luo, Jinjin Li
Read original paper ↗ ·Teaching free energy calculations to learn from experimental data
bioRxiv (Cold Spring Harbor Laboratory) · 2021
Marcus Wieder, Josh Fass, John D. Chodera
Read original paper ↗ ·Implementing a neural network interatomic model with performance portability for emerging exascale architectures
Computer Physics Communications · 2021
Saaketh Desai, Samuel Temple Reeve, James F. Belak
Read original paper ↗ ·Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction
IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) · 2021
Alain Tchagang, Julio J. Valdés
Read original paper ↗ ·Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer
arXiv (Cornell University) · 2021
K\"aser, Silvan, Markus Meuwly
Read original paper ↗ ·Alkali Metal Alkoxides in Noyori‐Type Hydrogenations
European Journal of Inorganic Chemistry · 2021
Pavel A. Dub
Read original paper ↗ ·