Papers citing Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Targeted Free Energy Perturbation Revisited: Accurate Free Energies from Mapped Reference Potentials
The Journal of Physical Chemistry Letters · 2021
Andrea Rizzi, Paolo Carloni, Michele Parrinello
Read original paper ↗ ·Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Journal of Chemical Theory and Computation · 2021
Viktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner
Read original paper ↗ ·DL_FFLUX: A Parallel, Quantum Chemical Topology Force Field
Journal of Chemical Theory and Computation · 2021
Benjamin C. B. Symons, Michael Bane, Paul L. A. Popelier
Read original paper ↗ ·Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies
Journal of Chemical Theory and Computation · 2021
Saientan Bag, Manuel Konrad, Tobias Schlöder, Pascal Friederich, Wolfgang Wenzel
Read original paper ↗ ·Entropy-based active learning of graph neural network surrogate models for materials properties
The Journal of Chemical Physics · 2021
Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam
Read original paper ↗ ·Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution
Journal of Chemical Theory and Computation · 2021
Jinzhe Zeng, Timothy J. Giese, Şölen Ekesan, Darrin M. York
Read original paper ↗ ·Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data
Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · 2021
Clemens Heistracher, Anahid Jalali, Indu Strobl, Axel Suendermann, Sebastian Meixner, Stephanie Holly, Daniel Schall, Bernhard Haslhofer, Jana Kemnitz
Read original paper ↗ ·Introductory Roadmap to Current Reactive Force-Field Methodologies
Springer eBooks · 2021
Caitlin Gibson Bresnahan, Glen R. Jenness, Revati Kumar, Manoj K. Shukla
Read original paper ↗ ·The Stochastic Augmented Lagrangian method for domain adaptation
Knowledge-Based Systems · 2021
Zhanhong Jiang, Chao Liu, Young M. Lee, Chinmay Hegde, Soumik Sarkar, Dongxiang Jiang
Read original paper ↗ ·Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data
Venue not supplied by source · 2021
Clemens Heistracher, Anahid Jalali, Indu Strobl, Axel Suendermann, Sebastian Meixner, Stephanie Holly, Daniel Schäll, Bernhard Haslhofer, Jana Kemnitz
Read original paper ↗ ·Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data
Venue not supplied by source · 2021
Clemens Heistracher, Anahid Jalali, Indu Strobl, Axel Suendermann, Sebastian Meixner, Stephanie Holly, Daniel Schäll, Bernhard Haslhofer, Jana Kemnitz
Read original paper ↗ ·OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
The Journal of Chemical Physics · 2021
Anders Steen Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O’Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, Thomas F. Miller
Read original paper ↗ ·Uncertainty quantification in machine learning and nonlinear least squares regression models
AIChE Journal · 2021
Ni Zhan, John R. Kitchin
Read original paper ↗ ·Free Energy Methods in Drug Discovery—Introduction
ACS symposium series · 2021
Zoe Cournia, Christophe J. Chipot, Benoı̂t Roux, Darrin M. York, Woody Sherman
Read original paper ↗ ·A conceptual study of transfer learning with linear models for data-driven property prediction
Computers & Chemical Engineering · 2021
Bowen Li, Srinivas Rangarajan
Read original paper ↗ ·Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries
Advanced Energy Materials · 2021
Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, Gábor Cśanyi, Robert Dominko, Kersti Hermansson, M. Rosa Palacín, Helge Sören Stein, Tejs Vegge
Read original paper ↗ ·Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
Nature Communications · 2021
Stephan Thaler, Julija Zavadlav
Read original paper ↗ ·Artificial intelligence-enhanced quantum chemical method with broad applicability
Nature Communications · 2021
Peikun Zheng, R.I. Zubatyuk, Wei Wu, Olexandr Isayev, Pavlo O. Dral
Read original paper ↗ ·A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion
Modelling and Simulation in Materials Science and Engineering · 2021
Yinan Wang, Linfeng Zhang, Ben Lian Xu, Xiaoyang Wang, Han Wang
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 ↗ ·Molecular Energies Derived from Deep Learning: Application to the Prediction of Formation Enthalpies Up to High Energy Compounds
Molecular Informatics · 2021
Didier Mathieu
Read original paper ↗ ·Resolving Chemical Dynamics in Biological Energy Conversion: Long-Range Proton-Coupled Electron Transfer in Respiratory Complex I
Accounts of Chemical Research · 2021
Ville R. I. Kaila
Read original paper ↗ ·Artificial intelligence: machine learning for chemical sciences
Journal of Chemical Sciences · 2021
Akshaya Karthikeyan, U. Deva Priyakumar
Read original paper ↗ ·3-D Hybrid CNN Combined With 3-D Generative Adversarial Network for Wetland Classification With Limited Training Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2022
Ali Jamali, Masoud Mahdianpari, Fariba Mohammadimanesh, Brian Brisco, Bahram Salehi
Read original paper ↗ ·A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential
Digital Discovery · 2022
Chen Qu, Qi Yu, Riccardo Conte, Paul L. Houston, Apurba Nandi, Joel M. Bowman
Read original paper ↗ ·Artificial intelligence–enabled fuel design
Elsevier eBooks · 2022
Kiran K. Yalamanchi, André Nicolle, S. Mani Sarathy
Read original paper ↗ ·Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost
Chemical Science · 2022
Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·End-to-end differentiable construction of molecular mechanics force fields
Chemical Science · 2022
Yuanqing Wang, Josh Fass, Benjamin Kaminow, John E. Herr, Dominic A. Rufa, Ivy Zhang, Iván Pulido, Michael M. Henry, Hannah E. Bruce Macdonald, Kenichiro Takaba, John D. Chodera
Read original paper ↗ ·Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers
Chemical Science · 2022
Daniel Bosch, Jun Wang, Lluı́s Blancafort
Read original paper ↗ ·Introduction
Elsevier eBooks · 2022
Balaji Mohan, Pinaki Pal, Jihad Badra, Yuanjiang Pei, Sibendu Som
Read original paper ↗ ·MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations
Chemical Science · 2022
Sanha Lee, Kristaps Ermanis, Jonathan M. Goodman
Read original paper ↗ ·Predicting reaction conditions from limited data through active transfer learning
Chemical Science · 2022
Eunjae Shim, Joshua A. Kammeraad, Ziping Xu, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman
Read original paper ↗ ·Prediction of protein p K a with representation learning
Chemical Science · 2022
Hatice Gökcan, Olexandr Isayev
Read original paper ↗ ·Reaction dynamics of Diels–Alder reactions from machine learned potentials
Physical Chemistry Chemical Physics · 2022
Tom A. Young, Tristan Johnston-Wood, Hanwen Zhang, Fernanda Duarte
Read original paper ↗ ·Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning
Energy & Environmental Science · 2022
Guijun Ma, Songpei Xu, Benben Jiang, Cheng Cheng, Xin Ping Ou Yang, Yue Shen, Tao Yang, Yunhui Huang, Han Ding, Ye Yuan
Read original paper ↗ ·Spin-component-scaled and dispersion-corrected second-order Møller–Plesset perturbation theory: a path toward chemical accuracy
Physical Chemistry Chemical Physics · 2022
Chandler Greenwell, Jan Řezáč, Gregory J. O. Beran
Read original paper ↗ ·光谱域编码的压缩光谱测量技术
Infrared and Laser Engineering · 2022
徐亦静 Xu Yijing, 吴志鹏 Wu Zhipeng, 王琦龙 Wang Qilong
Read original paper ↗ ·Correlation between the Stability of Substituted Cobaltocenium and Molecular Descriptors
The Journal of Physical Chemistry A · 2022
Shehani T. Wetthasinghe, Chunyan Li, Huina Lin, Tianyu Zhu, Chuanbing Tang, Vitaly A. Rassolov, Qi Wang, Sophya V. Garashchuk
Read original paper ↗ ·Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method
Journal of Chemical Information and Modeling · 2022
Solmaz Azimi, Sheenam Khuttan, Joe Z. Wu, Rajat Kumar Pal, Emilio Gallicchio
Read original paper ↗ ·Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials
Research Square · 2022
Gang Seob Jung, Hoon Joo Myung, Stephan Irle
Read original paper ↗ ·Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials
Journal of Chemical Theory and Computation · 2022
Dongxiao Hao, Xibing He, Adrián E. Roitberg, Shengli Zhang, Junmei Wang
Read original paper ↗ ·Accurate determination of solvation free energies of neutral organic compounds from first principles
Nature Communications · 2022
Leonid B. Pereyaslavets, Ganesh Kamath, Oleg Butin, Alexey Illarionov, Michael A. Olevanov, Igor V. Kurnikov, Serzhan N. Sakipov, Igor V. Leontyev, Е. Н. Воронина, Tyler Gannon, Grzegorz Nawrocki, Mikhail Darkhovskiy, Ilya Ivahnenko, Alexander Kostikov, Jessica Scaranto, Maria G. Kurnikova, Suvo Banik, Henry Chan, Michael Sternberg, Subramanian K. R. S. Sankaranarayanan, Brad Crawford, Jeffrey J. Potoff, Michael Levitt, Roger D. Kornberg, Boris Fain
Read original paper ↗ ·TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics
Journal of Chemical Information and Modeling · 2022
K. Brajesh, Vishnu Sresht, Qingyi Yang, Ray J. Unwalla, Meihua Tu, Alan M. Mathiowetz, Gregory A. Bakken
Read original paper ↗ ·Elucidating the temperature and density dependence of silver chloride hydration numbers in high-temperature water vapor: A first-principles molecular simulation study
Chemical Geology · 2022
Richard A. Messerly, Tae Jun Yoon, Ryan B. Jadrich, Robert P. Currier, Katie A. Maerzke
Read original paper ↗ ·Using machine learning as a surrogate model for agent-based simulations
PLoS ONE · 2022
Claudio Angione, Eric Silverman, Elisabeth Yaneske
Read original paper ↗ ·Phase transitions of zirconia: Machine-learned force fields beyond density functional theory
Physical review. B./Physical review. B · 2022
Peitao Liu, Carla Verdi, Ferenc Karsai, Georg Kresse
Read original paper ↗ ·Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
Journal of Chemical Theory and Computation · 2022
Raimón Fabregat, Alberto Fabrizio, Edgar A. Engel, Benjamin Meyer, Veronika Jurásková, Michele Ceriotti, Clémence Corminbœuf
Read original paper ↗ ·Zeo-1, a computational data set of zeolite structures
Scientific Data · 2022
Leonid Komissarov, Toon Verstraelen
Read original paper ↗ ·Exploring cryo-electron microscopy with molecular dynamics
Biochemical Society Transactions · 2022
John W. Vant, Daipayan Sarkar, Jonathan Nguyen, Alexander T. Baker, Josh V. Vermaas, Abhishek Singharoy
Read original paper ↗ ·Emerging Landscape of Computational Modeling in Pharmaceutical Development
Journal of Chemical Information and Modeling · 2022
Yuriy A. Abramov, Guangxu Sun, Qun Zeng
Read original paper ↗ ·