Fetching the paper…
Reading the bibliography…
Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks.
Self-consistent molecular-orbital methods. ix. an extended gaussian-type basis for molecular-orbital studies of organic molecules
Ditchfield, R., Hehre, W. J. & Pople, J. A · 1971
Earlier work this paper cites.
Linear representations of finite groups , vol. 42 (Springer, 1977)
Serre, J.-P. et al · 1977
Earlier work this paper cites.
Current status of transition-state theory
Truhlar, D. G., Garrett, B. C. & Klippenstein, S. J · 1996
Earlier work this paper cites.
Evaluation of transition state properties by density functional theory
Durant, J. L · 1996
Earlier work this paper cites.
A climbing image nudged elastic band method for finding saddle points and minimum energy paths
Henkelman, G., Uberuaga, B. P. & Jónsson, H · 2000
Earlier work this paper cites.
Optimization methods for finding minimum energy paths
Sheppard, D., Terrell, R. & Henkelman, G · 2008
Earlier work this paper cites.
Systematic optimization of long-range corrected hybrid density functionals
Chai, J.-D. & Head-Gordon, M · 2008
Earlier work this paper cites.
Introduction to metal–organic frameworks
Zhou, H.-C., Long, J. R. & Yaghi, O. M · 2012
Earlier work this paper cites.
Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C. & Reymond, J.-L · 2012
Earlier work this paper cites.
Density-functional tight binding—an approximate density-functional theory method
Seifert, G. & Joswig, J.-O · 2012
Earlier work this paper cites.
First-principles study of the role of interconversion between no2, n2o4, cis-ono-no2, and trans-ono-no2 in chemical processes
Liu, W.-G. & Goddard, W. A. I · 2012
Earlier work this paper cites.
Discovering chemistry with an ab initio nanoreactor
Wang, L.-P. et al · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S · 2015
Earlier work this paper cites.
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
Mardirossian, N. & Head-Gordon, M · 2017
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
Earlier work this paper cites.
Methods for exploring reaction space in molecular systems
Dewyer, A. L., Argüelles, A. J. & Zimmerman, P. M · 2018
Earlier work this paper cites.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Thomas, N. et al · 2018
Earlier work this paper cites.
Exploration of reaction pathways and chemical transformation networks
Simm, G. N., Vaucher, A. C. & Reiher, M · 2019
Earlier work this paper cites.
The exploration of chemical reaction networks
Unsleber, J. P. & Reiher, M · 2020
Cited alongside, same era.
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
Zeng, J., Cao, L., Xu, M., Zhu, T. & Zhang, J. Z. H · 2020
Cited alongside, same era.
Kinbot: Automated stationary point search on potential energy surfaces
Van de Vijver, R. & Zádor, J · 2020
Cited alongside, same era.
Exploring chemical compound space with quantum-based machine learning
von Lilienfeld, O. A., Müller, K.-R. & Tkatchenko, A · 2020
Cited alongside, same era.
Generating transition states of isomerization reactions with deep learning
Pattanaik, L., Ingraham, J. B., Grambow, C. A. & Green, W. H · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A. & Abbeel, P · 2020
Transition1x - a dataset for building generalizable reactive machine learning potentials
Schreiner, M., Bhowmik, A., Vegge, T., Busk, J. & Winther, O · 2022
Later among the works it cites.
Neuralneb—neural networks can find reaction paths fast
Schreiner, M., Bhowmik, A., Vegge, T., Jørgensen, P. B. & Winther, O · 2022
Later among the works it cites.
Equivariant diffusion for molecule generation in 3D
Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M · 2022
Later among the works it cites.
Structure-based drug design with equivariant diffusion models
Schneuing, A. et al · 2022
Later among the works it cites.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y. et al · 2020
Cited alongside, same era.
Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry
Grambow, C. A., Pattanaik, L. & Green, W. H · 2020
Cited alongside, same era.
Deep learning of activation energies
Grambow, C. A., Pattanaik, L. & Green, W. H · 2020
Cited alongside, same era.
Equivariant flows: Exact likelihood generative learning for symmetric densities
Köhler, J., Klein, L. & Noé, F · 2020
Cited alongside, same era.
The non-adiabatic nanoreactor: towards the automated discovery of photochemistry
Pieri, E. et al · 2021
Cited alongside, same era.
Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks
Zhao, Q. & Savoie, B. M · 2021
Cited alongside, same era.
Park, J., Sung, G., Lee, S., Kang, S. & Park, C · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A. et al · 2022
Later among the works it cites.
Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at dft cost
Duan, C., Chu, D. B. K., Nandy, A. & Kulik, H. J · 2022
Later among the works it cites.
SE (3) equivariant graph neural networks with complete local frames
Du, W. et al · 2022
Later among the works it cites.
High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation
Unsleber, J. P. et al · 2023
Closest in time.
Exploring catalytic reaction networks with machine learning
Margraf, J. T., Jung, H., Scheurer, C. & Reuter, K · 2023
Closest in time.
Prediction of transition state structures of gas-phase chemical reactions via machine learning
Choi, S · 2023
Closest in time.
DiffDock: Diffusion steps, twists, and turns for molecular docking
Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T · 2023
Closest in time.
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Du, W. et al · 2023
Closest in time.
A transferable recommender approach for selecting the best density functional approximations in chemical discovery
Duan, C., Nandy, A., Meyer, R., Arunachalam, N. & Kulik, H. J · 2023
Closest in time.
Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M. & Le, M · 2023
Closest in time.
I 2 SB: Image-to-image schrödinger bridge
Liu, G.-H. et al · 2023
Closest in time.
Diffusion-based generative ai for exploring transition states from 2d molecular graphs (2023)
Kim, S., Woo, J. & Kim, W. Y · 2023
Closest in time.
Comprehensive exploration of graphically defined reaction spaces
Zhao, Q. et al · 2023
Closest in time.