Papers citing Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
npj Computational Materials · 2020-05-13
April M. Cooper, Johannes Kästner, Alexander Urban, Nongnuch Artrith
Read original paper ↗ ·Machine-Learning Coupled Cluster Properties through a Density Tensor Representation
The Journal of Physical Chemistry A · 2020-05-15
Benjamin G. Peyton, Connor G. Briggs, Ruhee D’Cunha, Johannes T. Margraf, T. Daniel Crawford
Read original paper ↗ ·Machine Learning in Chemistry
ACS in focus · 2020-05-19
Jon Paul Janet, Heather J. Kulik
Read original paper ↗ ·Learning from the Machine: Uncovering Sustainable Nanoparticle Design Rules
The Journal of Physical Chemistry C · 2020-05-20
Clyde A. Daly, Rigoberto Hernandez
Read original paper ↗ ·Structure prediction of surface reconstructions by deep reinforcement learning
Journal of Physics Condensed Matter · 2020-05-20
Søren Ager Meldgaard, Henrik Lund Mortensen, Mathias Jo̷rgensen, Bjørk Hammer
Read original paper ↗ ·Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields
ChemRxiv · 2020-05-28
Aayush Gupta
Read original paper ↗ ·Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields
ChemRxiv · 2020-05-28
Aayush Gupta
Read original paper ↗ ·First-principles description of intra-chain exciton migration in an oligo(para-phenylene vinylene) chain. I. Generalized Frenkel–Holstein Hamiltonian
The Journal of Chemical Physics · 2020-05-29
Robert Binder, Matteo Bonfanti, David Lauvergnat, Irène Burghardt
Read original paper ↗ ·Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields
ChemRxiv · 2020-06-01
Aayush Gupta
Read original paper ↗ ·Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials
The Journal of Physical Chemistry B · 2020-06-02
Ryan B. Jadrich, Jeffery A. Leiding
Read original paper ↗ ·Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective
The Journal of Chemical Physics · 2020-06-05
Fabien Brieuc, Christoph Schran, Felix Uhl, Harald Forbert, Dominik Marx
Read original paper ↗ ·Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
Journal of Chemical Theory and Computation · 2020-06-05
Xiaowei Xie, Kristin Aslaug Persson, David W. Small
Read original paper ↗ ·Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Chemical Reviews · 2020-06-10
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
Read original paper ↗ ·Exploring chemical compound space with quantum-based machine learning
Nature Reviews Chemistry · 2020-06-12
O. Anatole von Lilienfeld, Klaus‐Robert Müller, Alexandre Tkatchenko
Read original paper ↗ ·Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
Journal of Chemical Theory and Computation · 2020-06-16
Christian Devereux, Justin S. Smith, Kate K. Huddleston, Kipton M. Barros, R.I. Zubatyuk, Olexandr Isayev, Adrián E. Roitberg
Read original paper ↗ ·Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods
Chem · 2020-06-16
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar‐Zadeh, Yuchen Liu, Xingyi Guan, Teresa L. Head-Gordon
Read original paper ↗ ·TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
Journal of Chemical Information and Modeling · 2020-06-22
Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, Adrián E. Roitberg
Read original paper ↗ ·Efficient Cysteine Conformer Search with Bayesian Optimization
arXiv (Cornell University) · 2020-06-26
Lincan Fang, Esko Makkonen, Milica Todorović, Patrick Rinke, Xi Chen
Read original paper ↗ ·Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch
Journal of Chemical Theory and Computation · 2020-07-01
Guoqing Zhou, Benjamin T. Nebgen, Nicholas Lubbers, Walter Malone, Anders M. N. Niklasson, Sergei Tretiak
Read original paper ↗ ·Metabolite Structure Assignment Using In Silico NMR Techniques
Analytical Chemistry · 2020-07-01
Susanta Das, Arthur S. Edison, Kenneth M. Merz
Read original paper ↗ ·Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
Methods · 2020-07-06
Sebastian Raschka, Benjamin Kaufman
Read original paper ↗ ·Assessing conformer energies using electronic structure and machine learning methods
International Journal of Quantum Chemistry · 2020-07-09
Dakota L. Folmsbee, Geoffrey Hutchison
Read original paper ↗ ·Artificial Intelligence Applied to the Prediction of Organic Materials
Royal Society of Chemistry eBooks · 2020-07-15
Steven Bennett, Andrew Tarzia, Martijn A. Zwijnenburg, Kim E. Jelfs
Read original paper ↗ ·Representation Learning in Chemistry
Royal Society of Chemistry eBooks · 2020-07-15
Joshua Staker, Gabriel Flintz Fraga Marques, Jumana Dakka
Read original paper ↗ ·Transfer Learning for Drug Discovery
Journal of Medicinal Chemistry · 2020-07-16
Chenjing Cai, Shiwei Wang, Youjun Xu, Weilin Zhang, Ke Tang, Qi Ouyang, Luhua Lai, Jianfeng Pei
Read original paper ↗ ·Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning
Journal of Chemical Theory and Computation · 2020-07-17
Eric M. Collins, Krishnan Raghavachari
Read original paper ↗ ·Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules
Scientific Data · 2020-07-21
Peter C. St. John, Yanfei Guan, Yeonjoon Kim, Brian D. Etz, Seonah Kim, Robert S. Paton
Read original paper ↗ ·Thousands of reactants and transition states for competing E2 and S N 2 reactions
Machine Learning Science and Technology · 2020-07-21
Guido Falk von Rudorff, Stefan Heinen, Marco Bragato, O. Anatole von Lilienfeld
Read original paper ↗ ·AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials
The Journal of Chemical Physics · 2020-07-27
Zachary L. Glick, Derek P. Metcalf, Alexios Koutsoukas, Steven A. Spronk, Daniel L. Cheney, C. David Sherrill
Read original paper ↗ ·Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields
ChemRxiv · 2020-07-29
Aayush Gupta
Read original paper ↗ ·Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials
bioRxiv (Cold Spring Harbor Laboratory) · 2020-07-30
Dominic A. Rufa, Hannah E. Bruce Macdonald, Josh Fass, Marcus Wieder, Patrick Grinaway, Adrián E. Roitberg, Olexandr Isayev, John D. Chodera
Read original paper ↗ ·AI Applications through the Whole Life Cycle of Material Discovery
Matter · 2020-08-01
Jiali Li, Kaizhuo Lim, Haitao Yang, Zekun Ren, S. Raghavan, Po‐Yen Chen, Tonio Buonassisi, Xiaonan Wang
Read original paper ↗ ·Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning
Chem · 2020-08-01
Ignacio Funes‐Ardoiz, Franziska Schoenebeck
Read original paper ↗ ·Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy
The Journal of Physical Chemistry A · 2020-08-03
Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan
Read original paper ↗ ·Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
Scientific Reports · 2020-08-07
Nicholas Lubbers, Animesh Agarwal, Yu Chen, Soyoun Son, Mohamed Z. Mehana, Qinjun Kang, Satish Karra, Christoph Junghans, Timothy Clark Germann, Hari Viswanathan
Read original paper ↗ ·Atomistic structure learning algorithm with surrogate energy model relaxation
Physical review. B./Physical review. B · 2020-08-12
Henrik Lund Mortensen, Søren Ager Meldgaard, Malthe Kjær Bisbo, Mads-Peter Verner Christiansen, Bjørk Hammer
Read original paper ↗ ·Computational Prediction of Novel Ice Phases: A Perspective
The Journal of Physical Chemistry Letters · 2020-08-12
Chongqin Zhu, Yurui Gao, Weiduo Zhu, Yuan Liu, Joseph S. Francisco, Xiao Cheng Zeng
Read original paper ↗ ·Improving the generative performance of chemical autoencoders through transfer learning
Machine Learning Science and Technology · 2020-08-12
Nicolae C. Iovanac, Brett Matthew Savoie
Read original paper ↗ ·Modelling and understanding battery materials with machine-learning-driven atomistic simulations
Journal of Physics Energy · 2020-08-17
Volker L. Deringer
Read original paper ↗ ·Ultrafast Spectroscopy of Photoactive Molecular Systems from First Principles: Where We Stand Today and Where We Are Going
Journal of the American Chemical Society · 2020-08-25
Irene Conti, Giulio Cerullo, Artur Nenov, Marco Garavelli
Read original paper ↗ ·Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening
The Journal of Physical Chemistry Letters · 2020-08-31
Fang Liu, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?
arXiv (Cornell University) · 2020-09-25
Vincent E. Elfving, Benno W. Broer, Mark Webber, Jacob L Gavartin, Mathew David Halls, K. Patrick Lorton, Arteum D. Bochevarov
Read original paper ↗ ·OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
The Journal of Chemical Physics · 2020-09-25
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller
Read original paper ↗ ·Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
Nature Communications · 2020-09-25
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino, Jean‐Louis Reymond
Read original paper ↗ ·Computation sparks chemical discovery
Nature Communications · 2020-09-28
Read original paper ↗ ·End-to-End Differentiable Molecular Mechanics Force Field Construction
arXiv (Cornell University) · 2020-10-02
Yuanqing Wang, Josh Fass, Kaminow, Benjamin, Herr, John E., Rufa, Dominic, Zhang, Ivy, Pulido, Iván, Henry, Mike, John D. Chodera
Read original paper ↗ ·Machine learning a molecular Hamiltonian for predicting electron dynamics
International Journal of Dynamics and Control · 2020-10-06
Harish S. Bhat, Karnamohit Ranka, Christine M. Isborn
Read original paper ↗ ·Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
Journal of Computer-Aided Molecular Design · 2020-10-09
Tobias Morawietz, Nongnuch Artrith
Read original paper ↗ ·Quantum chemical accuracy from density functional approximations via machine learning
Nature Communications · 2020-10-16
Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus‐Robert Müller, Kieron Burke
Read original paper ↗ ·Site-Level Bioactivity of Small-Molecules from Deep-Learned Representations of Quantum Chemistry
The Journal of Physical Chemistry A · 2020-10-21
Kathryn Sarullo, Matthew K. Matlock, S. Joshua Swamidass
Read original paper ↗ ·