Research graph
References from Artificial intelligence for science in quantum, atomistic, and continuum systems. Local targets link to admitted publications; unresolved targets remain external evidence.
Accurate structure prediction of biomolecular interactions with AlphaFold 3
10.1038/s41586-024-07487-w · 2024 · External reference
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Open-Fold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
2022 · External reference
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Invariance principle meets information bottleneck for out-of-distribution generalization
2021 · External reference
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Lie point symmetry and physics in-formed networks
2023 · External reference
10.52202/068431-1723
10.52202/068431-1723 · External reference
Studies in molecular dynamics. I. General method
10.1063/1.1730376 · 1959 · External reference
Constraint-based analysis of a physics-guided kinetic energy density expansion
10.1002/qua.27005 · 2023 · External reference
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2022 · External reference
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2021 · External reference
Design of the plasma position and shape control in the ITER tokamak using in-vessel coils
10.1109/tps.2009.2021476 · 2009 · External reference
Deep evidential regression
2020 · External reference
The process of structure-based drug design
10.1016/j.chembiol.2003.09.002 · 2003 · External reference
Cormorant: covariant molecular neural networks
2019 · External reference
Unresolved reference
2017 · External reference
More is different
10.1126/science.177.4047.393 · 1972 · External reference
Exploring local rotation invariance in 3D CNNs with steerable filters
2019 · External reference
Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
10.1016/j.chempr.2020.12.009 · 2021 · External reference
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Crystal structure generation with autoregressive large language modeling
10.1038/s41467-024-54639-7 · 2024 · External reference
Adaptive activity monitoring with uncertainty quantification in switching Gaussian process models
2019 · External reference
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Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
10.1103/physrevb.96.014112 · 2017 · External reference
Gene ontology: tool for the unification of biology
10.1038/75556 · 2000 · External reference
Towards precision medicine
10.1038/nrg.2016.86 · 2016 · External reference
Parametric design of aircraft geometry using partial differential equations
10.1016/j.advengsoft.2008.08.001 · 2009 · External reference
Closed-loop optimization of fast-charging protocols for batteries with machine learning
10.1038/s41586-020-1994-5 · 2020 · External reference
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GEOM, energy-annotated molecular conformations for property prediction and molecular generation
10.1038/s41597-022-01288-4 · 2022 · External reference
GBPNet: universal geometric representation learning on protein structures
2022 · External reference
GotenNet: rethinking efficient 3D equivariant graph neural networks
2025 · External reference
Predicting properties of amorphous solids with graph network potentials
2023 · External reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · 2021 · External reference
Graph neural network for Hamiltonian-based material property prediction
10.1007/s00521-021-06616-0 · 2022 · External reference
Uncertainty quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball
10.1016/j.jcp.2022.111608 · 2022 · External reference
Multiscale mobility networks and the spatial spreading of infectious diseases
10.1073/pnas.0906910106 · 2009 · External reference
Explainability techniques for graph convolutional networks
2019 · External reference
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
10.1038/s41524-021-00575-9 · 2021 · External reference
Unveiling the predictive power of static structure in glassy systems
10.1038/s41567-020-0842-8 · 2020 · External reference
Well-tempered meta-dynamics: a smoothly converging and tunable free-energy method
10.1103/physrevlett.100.020603 · 2008 · External reference
Phonons and related crystal properties from density-functional perturbation theory
10.1103/revmodphys.73.515 · 2001 · External reference
Autoregressive neural-network wavefunctions for ab initio quantum chemistry
10.1038/s42256-022-00461-z · 2022 · External reference
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2016 · External reference
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10.52202/075280-2412
10.52202/075280-2412 · External reference
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
10.52202/068431-0830 · 2022 · External reference
Noise2self: Blind denoising by self-supervision
2019 · External reference
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
10.1038/s41467-022-29939-5 · 2022 · External reference
Density-functional exchange-energy approximation with correct asymptotic behavior
10.1103/physreva.38.3098 · 1988 · External reference
Density-functional thermochemistry. III. The role of exact exchange
10.1063/1.464913 · 1993 · External reference
An introduction to spontaneous symmetry breaking
2019 · External reference
Perspective: machine learning potentials for atomistic simulations
10.1063/1.4966192 · 2016 · External reference
Generalized neural-network representation of high-dimensional potential-energy surfaces
10.1103/physrevlett.98.146401 · 2007 · External reference
B-spline CNNs on lie groups
2020 · External reference
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2018 · External reference
Combining differentiable PDE solvers and graph neural networks for fluid flow prediction
2020 · External reference
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2019 · External reference
Divergence-free smoothed particle hydrodynamics
2015 · External reference
Adaptive mesh refinement for hyperbolic partial differential equations
10.1016/0021-9991(84)90073-1 · 1984 · External reference
Announcing the worldwide protein data bank
10.1038/nsb1203-980 · 2003 · External reference
The protein data bank
10.1093/nar/28.1.235 · 2000 · External reference
Unresolved reference
2022 · External reference
Size-invariant graph representations for graph classification extrapolations
2021 · External reference
Model reduction and neural networks for parametric PDEs
10.5802/smai-jcm.74 · 2021 · External reference
A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
10.1016/j.ensm.2019.06.011 · 2019 · External reference
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10.63317/32y85i5g9gso
10.63317/32y85i5g9gso · External reference
Quantifying the chemical beauty of drugs
10.1038/nchem.1243 · 2012 · External reference
10.1063/5.0208746
10.1063/5.0208746 · External reference
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2006 · External reference
Variational inference: a review for statisticians
10.1080/01621459.2017.1285773 · 2017 · External reference
Weight uncertainty in neural network
2015 · External reference
Autonomously revealing hidden local structures in supercooled liquids
10.1038/s41467-020-19286-8 · 2020 · External reference
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Quantum chemical accuracy from density functional approximations via machine learning
10.1038/s41467-020-19093-1 · 2020 · External reference
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BioMedLM
2022 · External reference
PubChem3D: a new resource for scientists
10.1186/1758-2946-3-32 · 2011 · External reference
Learnable Bernoulli dropout for Bayesian deep learning
2020 · External reference
Bayesian proper orthogonal decomposition for learnable reduced-order models with uncertainty quantification
10.1109/tai.2023.3268609 · 2023 · External reference
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Spherical Fourier neural operators: learning stable dynamics on the sphere
2023 · External reference
AirfRANS: high fidelity computational fluid dynamics dataset for approximating Reynolds-averaged Navier-Stokes solutions
2022 · External reference
Zur quantentheorie der molekeln
10.1002/andp.19273892002 · 1927 · External reference
SE(3)-stochastic flow matching for protein backbone generation
2023 · External reference
Adaptive machine learning frame-work to accelerate ab initio molecular dynamics
10.1002/qua.24836 · 2015 · External reference
MAgnet: mesh agnostic neural PDE solver
2022 · External reference
10.1002/0470072768
10.1002/0470072768 · 2007 · External reference
Unresolved reference
2011 · External reference
Electronic wave functions - I. A general method of calculation for the stationary states of any molecular system
10.1098/rspa.1950.0036 · 1950 · External reference
Permutationally invariant potential energy surfaces in high dimensionality
10.1080/01442350903234923 · 2009 · External reference
JAX: composable transformations of Python+NumPy programs
2018 · External reference
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Geometric and physical quantities improve E(3) equivariant message passing
2022 · External reference
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Lie point symmetry data augmentation for neural PDE solvers
2022 · External reference
Message passing neural PDE solvers
2022 · External reference
Bypassing the Kohn-Sham equations with machine learning
10.1038/s41467 · 2017 · External reference
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Quantum and classical studies of vibrational motion of CH 5+ on a global potential energy surface obtained from a novel ab initio direct dynamics approach
10.1063/1.1775767 · 2004 · External reference
MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation
10.1002/jcc.26732 · 2021 · External reference
Language models are few-shot learners
2020 · External reference
10.1017/9781009089517
10.1017/9781009089517 · 2022 · External reference
Machine learning for partial differential equations
2023 · External reference
Machine learning for fluid mechanics
10.1146/annurev-fluid-010719-060214 · 2020 · External reference
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
10.1073/pnas.1517384113 · 2016 · External reference
Delocalization error: the greatest outstanding challenge in density-functional theory
2023 · External reference
Perspective on density functional theory
10.1063/1.4704546 · 2012 · External reference
Machine learning for molecular and materials science
10.1038/s41586-018-0337-2 · 2018 · External reference
CIDER: an expressive, nonlocal feature set for machine learning density functionals with exact constraints
10.1021/acs.jctc.1c00904 · 2022 · External reference
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Specification of molecular chirality
10.1002/anie.196603851 · 1966 · External reference
DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity
10.1093/bioinformatics/btac154 · 2022 · External reference
Binding site-enhanced sequence pretraining and out-of-cluster meta-learning predict genome-wide chemical-protein interactions for dark proteins
2022 · External reference
End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins
10.1371/journal.pcbi.1010851 · 2023 · External reference
Approximating quantum many-body wave functions using artificial neural networks
10.1103/physrevb.97.035116 · 2018 · External reference
On the convergence of SCF algorithms for the Hartree-Fock equations
10.1051/m2an:2000102 · 2000 · External reference
Choose a transformer: Fourier or Galerkin
2021 · External reference
Solving the quantum many-body problem with artificial neural networks
10.1126/science.aag2302 · 2017 · External reference
10.2172/2377336
10.2172/2377336 · External reference
La théorie des groupes fnis et continus et la géométrie diférentielle traitées par la méthode du repère mobile
1937 · External reference
Discovering quantum phase transitions with fermionic neural networks
10.1103/physrevlett.130.036401 · 2023 · External reference
New cubic perovskites for one-and two-photon water splitting using the computational materials repository
10.1039/c2ee22341d · 2012 · External reference
Computational screening of perovskite metal oxides for optimal solar light capture
10.1039/c1ee02717d · 2012 · External reference
Do large language models understand chemistry? A conversation with ChatGPT
10.1021/acs.jcim.3c00285 · 2023 · External reference
10.52202/079017-0826
10.52202/079017-0826 · External reference
Monte Carlo simulation of a many-fermion study
10.1103/physrevb.16.3081 · 1977 · External reference
Fermion nodes
10.1007/bf01030009 · 1991 · External reference
Ground state of the electron gas by a stochastic method
10.1103/physrevlett.45.566 · 1980 · External reference
A program to build E(N)-equivariant steerable CNNs
2022 · External reference
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Open catalyst 2020 (OC20) dataset and community challenges
10.1021/acscatal.0c04525 · 2021 · External reference
Defects and disorder in metal organic frameworks
10.1039/c5dt04392a · 2016 · External reference
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A universal graph deep learning interatomic potential for the periodic table
10.1038/s43588-022-00349-3 · 2022 · External reference
Graph networks as a universal machine learning framework for molecules and crystals
10.1021/acs.chemmater.9b01294 · 2019 · External reference
The rise of deep learning in drug discovery
10.1016/j.drudis.2018.01.039 · 2018 · External reference
Systematic improvement of neural network quantum states using Lanczos
2023 · External reference
Equivalence of restricted Boltzmann machines and tensor network states
10.1103/physrevb.97.085104 · 2018 · External reference
Projected Stein variational gradient descent
2020 · External reference
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Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
10.1109/72.392253 · 1995 · External reference
A simple framework for contrastive learning of visual representations
2020 · External reference
Calculation of cyclodextrin binding affinities: energy, entropy, and implications for drug design
2004 · External reference
Machine learning implicit solvation for molecular dynamics
10.1063/5.0059915 · 2021 · External reference
When does group invariant learning survive spurious correlations?
10.52202/068431-0510 · 2022 · External reference
Learning causally invariant representations for out-of-distribution generalization on graphs
2022 · External reference
Direct prediction of phonon density of states with Euclidean neural networks
10.1002/advs.202004214 · 2021 · External reference
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Symplectic recurrent neural networks
2020 · External reference
Group SELFIES: a robust fragment-based molecular string representation
10.1039/d3dd00012e · 2023 · External reference
Direct prediction of inelastic neutron scattering spectra from the crystal structure*
10.1088/2632-2153/acb315 · 2023 · External reference
A review of drug isomerism and its significance
10.4103/2229-516x.112233 · 2013 · External reference
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Machine learning of accurate energy-conserving molecular force fields
10.1126/sciadv.1603015 · 2017 · External reference
Towards exact molecular dynamics simulations with machine-learned force fields
10.1038/s41467-018-06169-2 · 2018 · External reference
Accurate global machine learning force fields for molecules with hundreds of atoms
10.1126/sciadv.adf0873 · 2023 · External reference
Hypernetwork-based meta-learning for low-rank physics-informed neural networks
2024 · External reference
Prediction of transition state structures of gas-phase chemical reactions via machine learning
10.1038/s41467-023-36823-3 · 2023 · External reference
10.1021/acs.jctc.3c00704
10.1021/acs.jctc.3c00704 · External reference
Symmetries and many-body excitations with neural-network quantum states
10.1103/physrevlett.121.167204 · 2018 · External reference
Fermionic neural-network states for ab-initio electronic structure
10.1038/s41467-020-15724-9 · 2020 · External reference
Two-dimensional frustrated J1-J2 model studied with neural network quantum states
10.1103/physrevb.100.12 · 2019 · External reference
Atomistic line graph neural network for improved materials property predictions
10.1038/s41524-021-00650-1 · 2021 · External reference
10.1038/s41524-024-01259-w
10.1038/s41524-024-01259-w · External reference
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On the role of gradients for machine learning of molecular energies and forces
2020 · External reference
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Automated prediction of lattice parameters from X-ray powder diffraction patterns
10.1107/s1600576721010840 · 2021 · External reference
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The best of the 20th century: Editors name top 10 algorithms
2000 · External reference
10.1017/cbo9781139031783
10.1017/cbo9781139031783 · 2016 · External reference
Group equivariant convolutional networks
2016 · External reference
Steerable CNNs
2017 · External reference
A general theory of equivariant CNNs on homogeneous spaces
2019 · External reference
Spherical CNNs
2018 · External reference
Active learning with statistical models
10.1613/jair.295 · 1996 · External reference
MEDNERF: Medical neural radiance fields for reconstructing 3D-aware CT-projections from a single X-ray
2022 · External reference
CrystalMELA: A new crystallographic machine learning platform for crystal system determination
10.1107/s1600576723000596 · 2023 · External reference
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Über die partiellen differenzengleichungen der mathematischen physik
10.1007/bf01448839 · 1928 · External reference
3-D inorganic crystal structure generation and property prediction via representation learning
10.1021/acs.jcim.0c00464 · 2020 · External reference
Evaluating the robustness of interpretability methods through explanation invariance and equivariance
10.52202/075280-3127 · 2023 · External reference
Lagrangian neural networks
2020 · External reference
Seven useful questions in density functional theory
10.1007/s11005-023-01665-z · 2023 · External reference
Pairwise supervised hashing with Bernoulli variational auto-encoder and self-control gradient estimator
2020 · External reference
Learning integrable dynamics with action-angle networks
2022 · External reference
SIFTS: Updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
10.1093/nar/gky1114 · 2019 · External reference
Robust deep learning-based protein sequence design using ProteinMPNN
10.1126/science.add2187 · 2022 · External reference
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Supercooled liquids and the glass transition
10.1038/35065704 · 2001 · External reference
Magnetic control of tokamak plasmas through deep reinforcement learning
10.1038/s41586-021-04301-9 · 2022 · External reference
Phonon density of states and heat capacity of La3−xTe4
10.1103/physrevb.80.184302 · 2009 · External reference
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
2023 · External reference
OpenFWI: large-scale multi-structural benchmark datasets for full waveform inversion
2022 · External reference
Vector neurons: a general framework for SO(3)-equivariant networks
2021 · External reference
Quantum entanglement in neural network states
10.1103/physrevx.7.021021 · 2017 · External reference
Machine learning based inter-atomic potential for amorphous carbon
10.1103/physrevb.95.094203 · 2017 · External reference
Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics
10.1021/acs.jpclett.8b00902 · 2018 · External reference
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BERT: pre-training of deep bidirectional transformers for language understanding
2019 · External reference
Graph neural networks as gradient flows: understanding graph convolutions via energy
2023 · External reference
Learning from the density to correct total energy and forces in first principle simulations
10.1063/1.5114618 · 2019 · External reference
Machine learning accurate ex-change and correlation functionals of the electronic density
10.1038/s41467-020-17265-7 · 2020 · External reference
Highly accurate and con-strained density functional obtained with differentiable program-ming
10.1103/physrevb.104.l161109 · 2021 · External reference
Solving the multiple instance problem with axis-parallel rectangles
10.1016/s0004-3702(96)00034-3 · 1997 · External reference
Quantum mechanics of many-electron systems
1929 · External reference
TorchMD: A deep learning framework for molecular simulations
10.1021/acs.jctc.0c01343 · 2021 · External reference
An image is worth 16 × 16 words: transformers for image recognition at scale
2021 · External reference
Machine learning for neutron scattering at ORNL
2020 · External reference
Atomic cluster expansion for accurate and transfer-able interatomic potentials
10.1103/physrevb.99.014104 · 2019 · External reference
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2012 · External reference
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2008 · External reference
Functional optimization of Fluidic devices with differentiable stokes flow
10.1145/3414685.3417795 · 2020 · External reference
10.52202/075280-2910
10.52202/075280-2910 · External reference
SE (3) equivariant graph neural networks with complete local frames
2022 · External reference
M2Hub: Unlocking the potential of machine learning for materials discovery
2023 · External reference
The trRosetta server for fast and accurate protein structure prediction
10.1038/s41596-021-00628-9 · 2021 · External reference
10.1038/s43588-023-00563-7
10.1038/s43588-023-00563-7 · External reference
A transferable recommender approach for selecting the best density functional approximations in chemical discovery
10.1038/s43588-022-00384-0 · 2023 · External reference
Bench-marking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
10.1038/s41524-020-00406-3 · 2020 · External reference
Efficient and scalable Bayesian neural nets with rank-1 factors
2020 · External reference
Atomic cluster expansion: completeness, efficiency and stability
10.1016/j.jcp.2022.110946 · 2022 · External reference
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On the universality of rotation equivariant point cloud networks
2025 · External reference
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SPICE, A dataset of drug-like molecules and peptides for training machine learning potentials
10.1038/s41597-022-01882-6 · 2023 · External reference
Optimal control of systems governed by partial differential equations
10.1112/blms/4.2.236 · 1972 · External reference
Translation between molecules and natural language
2022 · External reference
Text2Mol: cross-modal molecule retrieval with natural language queries
2021 · External reference
SynerGPT: in-context learning for personalized drug synergy prediction and drug design
10.1101/2023.07.06.547759 · 2023 · External reference
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1994 · External reference
Variational inference for graph convolutional networks in the absence of graph data and adversarial settings
2020 · External reference
10.1007/978-3-642-14090-7
10.1007/978-3-642-14090-7 · 2011 · External reference
Electronic excited states in deep variational Monte Carlo
10.1038/s41467-022-35534-5 · 2023 · External reference
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
10.1186/1758-2946-1-8 · 2009 · External reference
Spin-weighted spherical CNNS
2020 · External reference
Scaling spherical CNNs
2023 · External reference
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2022 · External reference
Protein complex prediction with AlphaFold-Multimer
10.1101/2021.10.04.463034 · 2021 · External reference
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Continuous-discrete convolution for geometry-sequence modeling in proteins
2023 · External reference
Bayesian attention modules
2020 · External reference
Gaussian process with graph convolutional Kernel for relational learning
2021 · External reference
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PotentialNet for molecular property prediction
10.1021/acscentsci.8b00507 · 2018 · External reference
Excited-state calculations with quantum Monte Carlo
2020 · External reference
Neural Gutzwiller-projected variational wave functions
10.1103/physrevb.100.125131 · 2019 · External reference
Unresolved reference
2011 · External reference
The Feynman lectures on physics; Volume I
10.1119/1.1972241 · 1965 · External reference
Deep dive into machine learning density functional theory for materials science and chemistry
10.1103/physrevmaterials.6.040301 · 2022 · External reference
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Residual pathway priors for soft equivariance constraints
2021 · External reference
10.1007/3-540-37072-2
10.1007/3-540-37072-2 · 2003 · External reference
Einfuss der confguration auf die wirkung der enzyme
10.1002/cber.18940270364 · 1894 · External reference
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10.1007/978-0-387-78650-6
10.1007/978-0-387-78650-6 · 2008 · External reference
Quantum Monte Carlo simulations of solids
10.1103/revmodphys.73.33 · 2001 · External reference
Managing uncertainty in data-derived densities to accelerate density functional theory
2019 · External reference
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
10.1021/acs.jcim.0c00411 · 2020 · External reference
A Euclidean transformer for fast and stable machine learned force fields
10.1038/s41467-024-50620-6 · 2024 · External reference
Unresolved reference
2001 · External reference
Neural scaling of deep chemical models
10.26434/chemrxiv-2022-3s512 · 2022 · External reference
Semantic scholar
10.5195/jmla.2018.280 · 2018 · External reference
Machine-learned potentials for next-generation matter simulations
10.1038/s41563-020-0777-6 · 2021 · External reference
Gaussian, Vol. 09 (Revision D.01)
2009 · External reference
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Reinforced genetic algorithm for structure-based drug design
2022 · External reference
Mimosa: multi-constraint molecule sampling for molecule optimization
10.1609/aaai.v35i1.16085 · 2021 · External reference
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SE(3)-transformers: 3D roto-translation equivariant attention networks
2020 · External reference
Accurate and numerically efficient r2SCAN meta-generalized gradient approximation
10.1021/acs.jpclett.0c02405 · 2020 · External reference
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
10.1038/s41592-019-0666-6 · 2020 · External reference
De novo design of protein interactions with learned surface fingerprints
2023 · External reference
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
2016 · External reference
Concrete dropout
2017 · External reference
Deep Bayesian active learning with image data
2017 · External reference
Protnlm: Model-based natural language protein annotation
2022 · External reference
GeoMol: torsional geometric generation of molecular 3d conformer ensembles
2021 · External reference
Unsupervised domain adaptation by backpropagation
2015 · External reference
Domain-adversarial training of neural networks
2016 · External reference
Graph representation learning via Hard and channel-wise attention networks
10.1145/3292500.3330897 · 2019 · External reference
Ab-Initio potential energy surfaces by pairing GNNs with neural wave functions
2021 · External reference
Generalizing neural wave functions
2023 · External reference
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Sample efficiency matters: a benchmark for practical molecular optimization
2022 · External reference
Efficient representation of quantum many-body states with deep neural networks
10.1038/s41467-017-00705-2 · 2017 · External reference
PiFold: Toward effective and efficient protein inverse folding
2023 · External reference
Proteininvbench: benchmarking protein inverse folding on diverse tasks, models, and metrics
2024 · External reference
Learning to predict material structure from neutron scattering data
10.1109/bigdata47090.2019.9005968 · 2019 · External reference
GemNet: universal directional graph neural networks for molecules
2021 · External reference
Directional message passing for molecular graphs
2020 · External reference
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
2019 · External reference
Machine learning the derivative discontinuity of density-functional theory
10.1088/2632-2153/ac3149 · 2021 · External reference
e3nn: Euclidean neural networks
2022 · External reference
Unresolved reference
2022 · External reference
Comment on ‘Pushing the frontiers of density functionals by solving the fractional electron problem’
10.1126/science.abq3385 · 2022 · External reference
Big data of materials science: critical role of the descriptor
10.1103/physrevlett.114.105503 · 2015 · External reference
Neural message passing for quantum chemistry
2017 · External reference
Calculation of protein-ligand binding affinities
10.1146/annurev.biophys.36.040306.132550 · 2007 · External reference
Smoothed particle hydro-dynamics: theory and application to non-spherical stars
10.1093/mnras/181.3.375 · 1977 · External reference
Unresolved reference
2014 · External reference
Structure-based protein function prediction using graph convolutional networks
10.1038/s41467-021-23303-9 · 2021 · External reference
Bayesian neural networks: an introduction and survey
2020 · External reference
Simple GNN regularisation for 3D molecular property prediction and beyond
2022 · External reference
A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions
10.1039/c7cp04913g · 2017 · External reference
Automated structure-and sequence-based design of proteins for high bacterial expression and stability
10.1016/j.molcel.2016.06.012 · 2016 · External reference
Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning
10.1039/c8cp06433d · 2019 · External reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · 2018 · External reference
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
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