Papers citing Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning
arXiv (Cornell University) · 2020
Axel U. J. Lode, Rui Lin, Miriam B\"uttner, Luca Papariello, Camille Lévêque, R. Chitra, Marios C. Tsatsos, Dieter Jaksch, Paolo Molignini
Read original paper ↗ ·A general-purpose machine-learning force field for bulk and nanostructured phosphorus
Nature Communications · 2020
Volker L. Deringer, A. Miguel, Gábor Cśanyi
Read original paper ↗ ·Profiling SARS-CoV-2 Main Protease (MPRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields
ACS Combinatorial Science · 2020
Aayush Gupta, Huan‐Xiang Zhou
Read original paper ↗ ·Machine Learning in Nano-Scale Biomedical Engineering
IEEE Transactions on Molecular Biological and Multi-Scale Communications · 2020
Alexandros–Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Sotiris A. Tegos, Vasilis K. Papanikolaou, George K. Karagiannidis
Read original paper ↗ ·Emerging materials intelligence ecosystems propelled by machine learning
Nature Reviews Materials · 2020
Rohit Batra, Le Song, Rampi Ramprasad
Read original paper ↗ ·Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning
Energy storage materials · 2020
Haikuo Zhang, Zhilong Wang, Jiahao Ren, Jinyun Liu, Jinjin Li
Read original paper ↗ ·Machine Learning for Electronically Excited States of Molecules
Chemical Reviews · 2020
Julia Westermayr, Philipp Marquetand
Read original paper ↗ ·Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation
The Journal of Chemical Physics · 2020
Alberto Fabrizio, Ksenia R. Briling, David D. Girardier, Clémence Corminbœuf
Read original paper ↗ ·Unsupervised Assisted Directional Design of Chemical Reactions
Cell Reports Physical Science · 2020
Lin Zhang, Zhilong Wang, Zhiyun Wei, Jinjin Li
Read original paper ↗ ·Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments
Journal of Chemical Information and Modeling · 2020
Shae-Lynn J. Lahey, Nguyễn Thiên Phúc Từ, Christopher N. Rowley
Read original paper ↗ ·Mean-field density matrix decompositions
The Journal of Chemical Physics · 2020
Janus J. Eriksen
Read original paper ↗ ·DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
Journal of Chemical Theory and Computation · 2020
Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan
Read original paper ↗ ·Ab initio machine learning in chemical compound space
arXiv (Cornell University) · 2020
Bing Huang, O. Anatole von Lilienfeld
Read original paper ↗ ·Neural Networks Accelerate the Ab Initio Prediction of Solid–Solid Phase Transitions at High Pressures
The Journal of Physical Chemistry Letters · 2020
Yanqiang Han, Zhilong Wang, Jinjin Li
Read original paper ↗ ·Atomic permutationally invariant polynomials for fitting molecular force fields
Machine Learning Science and Technology · 2020
Alice E. A. Allen, Geneviève Dusson, Christoph Ortner, Gábor Cśanyi
Read original paper ↗ ·Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors
ChemRxiv · 2020
Rocco Meli, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris, Philip Charles Biggin
Read original paper ↗ ·TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics
ChemRxiv · 2020
Brajesh Rai, Vishnu Sresht, Qingyi Yang, Rayomond J. Unwalla, Meihua Tu, Alan M. Mathiowetz, Gregory A. Bakken
Read original paper ↗ ·A critical overview of computational approaches employed for COVID-19 drug discovery
Chemical Society Reviews · 2021
Eugene N. Muratov, Rommie E. Amaro, Carolina Horta Andrade, Nathan Brown, Sean Ekins, Denis Fourches, Olexandr Isayev, Dima Kozakov, José L. Medina‐Franco, Kenneth M. Merz, Tudor Ionel Oprea, Vladimir Poroikov, Gisbert Schneider, Matthew H. Todd, Alexandre A. Varnek, David Alan Winkler, Alexey V. Zakharov, Artem R Cherkasov, Alexander Tropsha
Read original paper ↗ ·An Improved Algorithm Based on Fast Search and Find of Density Peak Clustering for High‐Dimensional Data
Wireless Communications and Mobile Computing · 2021
Hui Du, Yiyang Ni, Zhihe Wang
Read original paper ↗ ·Computer-Aided Drug Design
Drug Discovery and Development · 2021
Prasad V. Bharatam
Read original paper ↗ ·Data-efficient machine learning for molecular crystal structure prediction
Chemical Science · 2021
Simon Wengert, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf
Read original paper ↗ ·Do robots dream of electronic structures?
Venue not supplied by source · 2021
Kevin Basemann
Read original paper ↗ ·Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution
Chemical Science · 2021
Marcus Wieder, Josh Fass, John D. Chodera
Read original paper ↗ ·IMPROVING NEURAL NETWORK PREDICTIONS OF MATERIAL PROPERTIES WITH LIMITED DATA USING TRANSFER LEARNING
Journal of Machine Learning for Modeling and Computing · 2021
Schuyler Krawczuk, Daniele Venturi
Read original paper ↗ ·Machine Learning Interatomic Force Fields for Carbon Allotropic Materials
Springer series in materials science · 2021
Xiangjun Liu, Quanjie Wang, Jie Zhang
Read original paper ↗ ·Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good
Communications in computer and information science · 2021
Paola A. Buitrago, Nicholas A. Nystrom
Read original paper ↗ ·Predicting phosphorescence energies and inferring wavefunction localization with machine learning
Chemical Science · 2021
Andrew E. Sifain, Levi Lystrom, Richard A. Messerly, Justin S. Smith, Benjamin T. Nebgen, Kipton M. Barros, Sergei Tretiak, Nicholas Lubbers, Brendan J. Gifford
Read original paper ↗ ·Real-time prediction of 1 H and 13 C chemical shifts with DFT accuracy using a 3D graph neural network
Chemical Science · 2021
Yanfei Guan, Shree Sowndarya S. V., Liliana C. Gallegos, Peter C. St. John, Robert S. Paton
Read original paper ↗ ·Trends in computational molecular catalyst design
Dalton Transactions · 2021
Ademola Soyemi, Tibor Szilvási
Read original paper ↗ ·TSNet: predicting transition state structures with tensor field networks and transfer learning
Chemical Science · 2021
Riley Jackson, Wenyuan Zhang, Jason Kenneth Pearson
Read original paper ↗ ·Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications
Journal of Chemical Theory and Computation · 2021
László Gyevi‐Nagy, Mihály Kállay, Péter R. Nagy
Read original paper ↗ ·Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases
Wiley Interdisciplinary Reviews Computational Molecular Science · 2021
José Peña Guerrero, Paul Nguewa, Alfonso T. García‐Sosa
Read original paper ↗ ·Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
Chem · 2021
Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli
Read original paper ↗ ·Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning
Journal of Chemical Theory and Computation · 2021
Pablo A. Unzueta, Chandler Greenwell, Gregory J. O. Beran
Read original paper ↗ ·Pure non-local machine-learned density functional theory for electron correlation
Nature Communications · 2021
Johannes T. Margraf, Karsten Reuter
Read original paper ↗ ·Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations.
arXiv (Cornell University) · 2021
Tian Xie, Arthur France‐Lanord, Yanming Wang, Jeffrey Lopez, Michael A. Stolberg, Megan R. Hill, Graham Leverick, Rafael Gómez‐Bombarelli, Jeremiah A. Johnson, Yang Shao‐Horn, Jeffrey C. Grossman
Read original paper ↗ ·Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers
The Journal of Chemical Physics · 2021
Chee‐Kong Lee, Chengqiang Lu, Yue Yu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Qi Liu, Liang Shi
Read original paper ↗ ·When do short-range atomistic machine-learning models fall short?
The Journal of Chemical Physics · 2021
Shuwen Yue, Maria Carolina Muniz, Marcos F. Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z. Panagiotopoulos
Read original paper ↗ ·Miniaturization of optical spectrometers
Science · 2021
Zongyin Yang, Tom Albrow‐Owen, Weiwei Cai, Tawfique Hasan
Read original paper ↗ ·Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
npj Computational Materials · 2021
Zhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li
Read original paper ↗ ·Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models
The Journal of Chemical Physics · 2021
Anders M. N. Niklasson
Read original paper ↗ ·Δ -machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory
The Journal of Chemical Physics · 2021
Apurba Nandi, Chen Qu, Paul L. Houston, Riccardo Conte, Joel M. Bowman
Read original paper ↗ ·Efficient Amino Acid Conformer Search with Bayesian Optimization
Journal of Chemical Theory and Computation · 2021
Lincan Fang, Esko Makkonen, Milica Todorović, Patrick Rinke, Xi Chen
Read original paper ↗ ·Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
The Journal of Chemical Physics · 2021
Tamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller
Read original paper ↗ ·Automated discovery of a robust interatomic potential for aluminum
Nature Communications · 2021
Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton M. Barros
Read original paper ↗ ·Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design
Research Square · 2021
Alain Tchagang, Ahmed H. Tewfik, Julio J. Valdés
Read original paper ↗ ·Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization
The Journal of Physical Chemistry A · 2021
Dakota L. Folmsbee, David Ryan Koes, Geoffrey Hutchison
Read original paper ↗ ·Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning
Journal of Chemical Information and Modeling · 2021
Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma
Read original paper ↗ ·Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution
ChemRxiv · 2021
Jinzhe Zeng, Timothy J. Giese, Şölen Ekesan, Darrin M. York
Read original paper ↗ ·Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
The Journal of Chemical Physics · 2021
Valentín Vassilev-Galindo, Grégory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
Read original paper ↗ ·