Papers citing Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Simplified Calibration System for Stereoradiography in Scoliosis
Studies in health technology and informatics · 2002
David Mitton, Raphaël Dumas, S. Laporte, Philippe Leborgne, Bataille Ph., D. Quidet, Wafa Skalli E
Read original paper ↗ ·Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies
Venue not supplied by source · 2019
Mojtaba Haghighatlari
Read original paper ↗ ·A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space
ChemRxiv · 2019
Mojtaba Haghighatlari, Gaurav Vishwakarma, Mohammad Atif Faiz Afzal, Johannes Hachmann
Read original paper ↗ ·A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space
ChemRxiv · 2019
Mojtaba Haghighatlari, Gaurav Vishwakarma, Mohammad Atif Faiz Afzal, Johannes Hachmann
Read original paper ↗ ·Physical machine learning outperforms "human learning" in Quantum Chemistry
arXiv (Cornell University) · 2019
Anton V. Sinitskiy, Vijay S. Pande
Read original paper ↗ ·Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Science Advances · 2019
R.I. Zubatyuk, Justin S. Smith, Jerzy Leszczyński, Olexandr Isayev
Read original paper ↗ ·Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning
Chemistry of Materials · 2019
Daniel W. Davies, Keith T. Butler, Aron Walsh
Read original paper ↗ ·Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
Advanced Materials · 2019
Volker L. Deringer, Miguel A. Caro, Gábor Cśanyi
Read original paper ↗ ·Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
Angewandte Chemie · 2019
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·Autonomous Discovery in the Chemical Sciences Part I: Progress
Angewandte Chemie International Edition · 2019
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Read original paper ↗ ·Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning
Journal of Chemical Theory and Computation · 2019
Lixue Cheng, Nikola Kovachki, Matthew Welborn, Thomas F. Miller
Read original paper ↗ ·The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
ChemRxiv · 2019
Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak
Read original paper ↗ ·Dataset’s chemical diversity limits the generalizability of machine learning predictions
Journal of Cheminformatics · 2019
Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy, Benoit Da Mota
Read original paper ↗ ·Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
Journal of Chemical Theory and Computation · 2019
Christoph Schran, Jörg Behler, Dominik Marx
Read original paper ↗ ·Machine learning and artificial neural network accelerated computational discoveries in materials science
Wiley Interdisciplinary Reviews Computational Molecular Science · 2019
Yang Hong, Bo Hou, Hengle Jiang, Jingchao Zhang
Read original paper ↗ ·Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications
Journal of Chemical Theory and Computation · 2019
László Gyevi‐Nagy, Mihály Kállay, Péter R. Nagy
Read original paper ↗ ·Opportunities for Artificial Intelligence in Advancing Precision Medicine
Current Genetic Medicine Reports · 2019
Fabian V. Filipp
Read original paper ↗ ·The art of atom descriptor design
Drug Discovery Today Technologies · 2019
Andreas H. Göller
Read original paper ↗ ·Schrodinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space
ChemRxiv · 2019
James Stevenson, Leif D. Jacobson, Yutong Zhao, Chuanjie Wu, Jon R. Maple, Karl Leswing, Edward D. Harder, Robert Abel
Read original paper ↗ ·Machine Learning Transition Temperatures from 2D Structure
Journal of Molecular Graphics and Modelling · 2019
Andrew E. Sifain, Betsy M. Rice, Samuel H. Yalkowsky, Brian C. Barnes
Read original paper ↗ ·A machine learning based intramolecular potential for a flexible organic molecule
Faraday Discussions · 2020
Daniel J. Cole, Letif Mones, Gábor Cśanyi
Read original paper ↗ ·Generating transition states of isomerization reactions with deep learning
Physical Chemistry Chemical Physics · 2020
Lagnajit Pattanaik, John Ingraham, Colin A. Grambow, William H. Green
Read original paper ↗ ·Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Reaction Chemistry & Engineering · 2020
Natalie S. Eyke, William H. Green, Klavs F. Jensen
Read original paper ↗ ·Simulating protein–ligand binding with neural network potentials
Chemical Science · 2020
Shae-Lynn J. Lahey, Christopher N. Rowley
Read original paper ↗ ·Automated in Silico Design of Homogeneous Catalysts
ACS Catalysis · 2020
Marco Foscato, Vidar Remi Jensen
Read original paper ↗ ·DT-LET: Deep transfer learning by exploring where to transfer
Neurocomputing · 2020
Jianzhe Lin, Liang Zhao, Qi Wang, Rabab Kreidieh Ward, Z. Jane Wang
Read original paper ↗ ·Machine learning for interatomic potential models
The Journal of Chemical Physics · 2020
Tim Mueller, Alberto Hernández, Chuhong Wang
Read original paper ↗ ·Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy
IEEE Electron Device Letters · 2020
Pinghui Mo, Mengchao Shi, Wenze Yao, Jie Liu
Read original paper ↗ ·Monitoring sugar crystallization with deep neural networks
Journal of Food Engineering · 2020
Jinlai Zhang, Yanmei Meng, Jianfan Wu, Johnny Qin, Hui Wang, Tao Yao, Shuangshuang Yu
Read original paper ↗ ·Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces
ChemRxiv · 2020
Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli
Read original paper ↗ ·Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces
ChemRxiv · 2020
Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli
Read original paper ↗ ·Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms
arXiv (Cornell University) · 2020
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar‐Zadeh, Yuchen Liu, Xingyi Guan, Teresa L. Head-Gordon
Read original paper ↗ ·Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation
Journal of Chemical Theory and Computation · 2020
WooSeok Jeong, Samuel J. Stoneburner, Daniel S. King, Ruye Li, Andrew Walker, Roland H. Lindh, Laura Gagliardi
Read original paper ↗ ·Machine-Learning Methods for Computational Science and Engineering
Computation · 2020
Michael Frank, Dimitris Drikakis, Vassilis Charissis
Read original paper ↗ ·Quantum Chemistry in the Age of Machine Learning
The Journal of Physical Chemistry Letters · 2020
Pavlo O. Dral
Read original paper ↗ ·Transforming solid-state precipitates via excess vacancies
Nature Communications · 2020
LAURE N. BOURGEOIS, Yong Zhang, Zezhong Zhang, Yiqiang Chen, Nikhil V. Medhekar
Read original paper ↗ ·Deep Learning for Optoelectronic Properties of Organic Semiconductors
The Journal of Physical Chemistry C · 2020
Chengqiang Lu, Qi Liu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Liang Shi, Chee‐Kong Lee
Read original paper ↗ ·Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks
Carbon · 2020
M.A.N. Dewapriya, R. K. N. D. Rajapakse, W. P. S. Dias
Read original paper ↗ ·Roadmap on multiscale materials modeling
Modelling and Simulation in Materials Science and Engineering · 2020
Erik Van der Giessen, Peter A. Schultz, Nicolas Bertin, Vasily V. Bulatov, Wei Cai, Gábor Cśanyi, Stephen M. Foiles, M.G.D. Geers, Carlos González, Markus Hütter, Woo Kyun Kim, Dennis M. Kochmann, Javier LLorca, Ann E. Mattsson, Jörg Rottler, Alexander L. Shluger, Ryan B. Sills, Ingo Steinbach, Alejandro H. Strachan, Ellad B. Tadmor
Read original paper ↗ ·Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials
Journal of Chemical Information and Modeling · 2020
John Vant, Shae-Lynn J. Lahey, Kalyanashis Jana, Mrinal Shekhar, Daipayan Sarkar, Barbara H. Munk, Ulrich Kleinekathöfer, Sumit Mittal, Christopher N. Rowley, Abhishek Singharoy
Read original paper ↗ ·Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy
The Journal of Physical Chemistry B · 2020
Zhilong Wang, Yanqiang Han, Jinjin Li, Xiao Dong He
Read original paper ↗ ·Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry
Journal of Chemical Theory and Computation · 2020
Raimón Fabregat, Alberto Fabrizio, Benjamin Meyer, Daniel Hollas, Clémence Corminbœuf
Read original paper ↗ ·Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction
Journal of Chemical Information and Modeling · 2020
Gabriele Scalia, Colin A. Grambow, Barbara Pernici, Yi‐Pei Li, William H. Green
Read original paper ↗ ·Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders
The Journal of Physical Chemistry A · 2020
Nicolae C. Iovanac, Brett Matthew Savoie
Read original paper ↗ ·Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique
The Journal of Physical Chemistry Letters · 2020
Qionghua Zhou, Shuaihua Lu, Yilei Wu, Jinlan Wang
Read original paper ↗ ·Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties
Trends in Chemistry · 2020
Tonghui Wang, Grit Kupgan, Jean‐Luc Brédas
Read original paper ↗ ·The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
ChemRxiv · 2020
Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak
Read original paper ↗ ·Co-segregation of Mg and Zn atoms at the planar η1-precipitate/Al matrix interface in an aged Al–Zn–Mg alloy
Scripta Materialia · 2020
Bingqing Cheng, Xiaojun Zhao, Yong Zhang, Houwen Chen, I. J. Polmear, Jian‐Feng Nie
Read original paper ↗ ·The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Scientific Data · 2020
Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak
Read original paper ↗ ·Using Machine Learning to Emulate Agent-Based Simulations
arXiv (Cornell University) · 2020
Claudio Angione, Eric Silverman, Elisabeth Yaneske
Read original paper ↗ ·