We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models a…
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48 results for “atomic computation”
Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculationsphysics.comp-ph
Machine learning predicts atomization energies accurately from low-fidelity calculations.
problem Predicting accurate atomization energies of organic molecules efficiently.
method Machine learning models trained on low-fidelity B3LYP energies to predict high-fidelity G4MP2 energies.
result Predicted G4MP2 atomization energies within 0.012 eV for molecules with 10-14 heavy atoms.
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential approach, we present LC-GAP, a new system for generating atomic potentials through machine learning (ML). Tests on the QM7, QM7b and GDB9 biom…
Atomistic structure learningcond-mat.mtrl-sci
ASLA learns atomic structures using neural networks and reinforcement learning.
problem Designing materials and drugs with desired properties.
method Atomistic structure learning algorithm (ASLA) using a convolutional neural network and reinforcement learning.
result ASLA can predict optimal structural arrangements of atoms for various target properties.
Distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. To mitigate these overheads, several studies propose the use of sparsified stochastic gradients. We argue that these are facets of a general sparsification method that can operate on any p…
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorscond-mat.mtrl-sci
Model predicts DFT formation energies without atomic positions.
problem Fast prediction of material properties without atomic positions.
method Symmetry-labeled graphs and message passing neural network.
result Mean absolute error below 0.1 eV for selenides.
Predicting molecular dipole moments by combining atomic partial charges and atomic dipolesphysics.chem-ph
MuML models predict molecular dipole moments using atomic partial charges and dipoles.
problem Predicting molecular dipole moments accurately and efficiently.
method Combining atomic partial charges and atomic dipoles within a physically inspired ML model.
result MuML models achieve excellent transferability and accuracy, approaching DFT results at a fraction of the computational cost.
A new machine-learned CG model predicts protein structures efficiently.
problem Developing a universal, computationally efficient protein simulation model.
method Combining deep learning with all-atom protein simulations to create a transferable CG force field.
result The model predicts protein structures, intermediates, and fluctuations efficiently.
Site-specific graph neural network for predicting protonation energy of oxygenate moleculesphysics.chem-ph
Graph neural network predicts protonation energies of oxygen atoms in bio-oil molecules.
problem Predicting protonation energies of oxygen atoms in bio-oil molecules for chemical upgrading.
method Site-specific graph neural network approach using iterative local nonlinear embedding.
result Effective prediction of protonation energies of individual oxygen atoms in bio-oil molecules.
A novel method computes Wigner kernels for atomic environments, achieving state-of-the-art accuracy.
problem Efficiently describing local atomic environments in materials science.
method Computes fully equivariant and body-ordered kernels iteratively, independent of basis.
result Achieves state-of-the-art accuracy on the QM9 benchmark dataset.
Optimal Invariant Bases for Atomistic Machine Learningphysics.chem-ph
Optimizes atomic descriptors to reduce redundancy and improve machine learning models.
problem Redundant descriptors in atomistic machine learning models increase computational burden and limit model expressivity.
method Employing techniques from pattern recognition, we refine and augment existing atomistic representations to produce optimal sets of descriptors.
result New architectures recognize up to 5-body patterns with low computational cost and high accuracy.
Geometric GNNs model 3D atomic systems with rotations and translations.
problem Modeling 3D atomic systems with geometric graphs and machine learning.
method Invariant, equivariant, and unconstrained GNN architectures.
result Geometric GNNs leverage physical symmetries and chemical properties.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexesq-bio.BM
New neural network predicts accurate protein complex structures.
problem Predicting accurate protein complex structures from atomic coordinates.
method Rotation-equivariant neural network combining point-based representation, equivariance, local convolutions, and hierarchical subsampling.
result Significant improvement in identifying accurate structural models.
Optimizes basis for density-based atomic representations to enhance compactness and accuracy.
problem Improving the efficiency and accuracy of machine learning models for atomic properties.
method An unsupervised approach to determine the optimal basis set for atom density representations using splines.
result Optimal basis sets that encode structural information more compactly and accurately.
Unified theory linking atom-centered and message-passing models for molecular properties.
problem Combining atom-centered and message-passing models for accurate molecular property prediction.
method Generalizing ACDC framework to include multi-centered information, providing a complete linear basis for regression.
result Unified understanding of atom-centered and message-passing models, providing a coherent foundation.
Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization. A scoring function that is differentiable with respect to atom positions can be used for both scoring and gradient-based optimization…
Electronic-structure properties from atom-centered predictions of the electron densityphysics.chem-ph
A new method predicts electron density accurately from atom-centered models.
problem Predicting electron density accurately from atom-centered models.
method Gradient-based approach to minimize loss function in an optimized sparse feature space.
result Extremely accurate predictions of electron density and total energies.
Deep learning predicts protein structures accurately.
problem Predicting the 3D structure of proteins from amino acid sequences.
method Embeddings and deep learning models for backbone atom distance matrices and torsion angles.
result Competitive results in CASP13 and CASP12, surpassing previous winners.
The atomic swap protocol allows for the exchange of cryptocurrencies on different blockchains without the need to trust a third-party. However, market participants who desire to hold derivative assets such as options or futures would also benefit from trustless exchange. In this paper I propose the atomic swaption, whi…
Researchers use manifold learning to analyze 4D-STEM data of graphene, revealing atomic structure details.
problem Challenges in processing and interpreting large 4D-STEM datasets, especially for light materials.
method Data-driven manifold learning approaches for visualization and exploration of 4D-STEM datasets.
result Extracted patterns relate to individual atom sites and sublattice structures, effectively discriminating single dopant anomalies.
Equivariant representations for molecular Hamiltonians and N-center atomic-scale propertiesphysics.chem-ph
New features for quantum calculations learn N-center Hamiltonian matrix elements.
problem Quantum calculations need features for N-center Hamiltonians, not just atom-centered ones.
method Developed fully equivariant N-center features for machine learning.
result Learned matrix elements of N-center Hamiltonians efficiently.
New model clusters graphs using Gromov-Wasserstein discrepancy.
problem Graph clustering with topological structures and node attributes.
method Gromov-Wasserstein discrepancy for relational graph comparison; learns atoms and weights via minimization of discrepancy.
result Model achieves flexible factorization of unaligned graphs with different sizes.
Neural network learns atomic coordinates from Patterson maps in a simplified case.
problem Training a neural network to infer atomic coordinates from Patterson maps.
method Synthetic data training, centering output maps, removing centrosymmetric inversion, and adding empty space.
result The network can generalize to infer atom positions from Patterson maps not in the training set.
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Predictionphysics.chem-ph
Study compares atom representations in graph neural networks for molecular properties.
problem Incorrect attribution of results in molecular property prediction due to varying atom features.
method Evaluated multiple atom representations on free energy, solubility, and metabolic stability predictions.
result Different atom representations can lead to varying predictive performance in graph neural networks.
Improved method for private quantile estimation in datasets with atoms.
problem Private estimation of quantiles in datasets with atoms.
method Application of Inverse Sensitivity mechanism and Heuristically Smoothed JointExp.
result Improved performance on datasets with atoms compared to existing methods.
Equivariant diffusion model generates 3D molecules efficiently.
problem Generating high-quality 3D molecules efficiently.
method Equivariant Diffusion Model (EDM) that operates on atom coordinates and types.
result Significantly outperforms previous methods in molecule quality and training efficiency.
We prove that the introduction of the class of geometrically atomic bundle maps by Harvey and Lawson in their theory of singular connections is not necessary because an arbitrary map satisfies the conditions of geometric atomicity.
Simple and efficient algorithms for training machine learning potentials to force dataphysics.comp-ph
New algorithm efficiently trains machine learning models to atomic forces data.
problem Efficiently training machine learning models to large amounts of force data.
method Developed an efficient algorithm for training machine learning models to all available force data.
result Training to all available force data is only a few times more expensive than training to energies alone.
DiAMoNDBack models protein backmapping from coarse-grained Cα traces.
problem Restoring all-atom details from coarse-grained protein representations.
method Autoregressive denoising diffusion model for residue-by-residue backmapping.
result Achieves state-of-the-art reconstruction performance in diverse applications.
PAM models generate dependent random distributions across groups with overlapping clusters.
problem Generating dependent random distributions across multiple groups.
method Atom skipping in an infinite mixture model.
result Interpretable posterior inference of cluster exclusivity and sharing.
Non-atomic arbitrage exploits price differences on Ethereum and other blockchains, accounting for over 10% of Ethereum's block value.
problem Price differences on decentralized exchanges and centralized exchanges lead to MEV.
method Analyzed non-atomic arbitrage on Ethereum's largest DEXes, identifying its prevalence and impact.
result More than 10% of Ethereum's block value is attributed to non-atomic arbitrage, involving over $132 billion.
Predicts atomization energy with high accuracy using graph kernels and active learning.
problem Predicting molecular atomization energy with high accuracy.
method Gaussian process regression with marginalized graph kernel, active learning.
result Achieves mean absolute error of 0.62 +- 0.01 kcal/mol with 2000 training samples.
Improved chemical predictions through compressed atomic species representations.
problem Intractable chemical space of molecules and materials.
method Introducing elemental modes for compressed representation of atomic species.
result Elemental modes enable improvements in machine learning tasks for chemical predictions.
Atoms meet symbolsmath.AG
Unified approach to invariants in equivariant geometry.
problem Constructing invariants in -equivariant birational geometry.
method Combining atom theory and modular symbols.
result Developed new geometric invariants for orbifolds and surfaces.
Improves molecular activity prediction using graph convolutional neural networks considering graph distances.
problem Predicting molecular activity using graph convolutional neural networks with improved distance representation.
method Proposed three improvements: modified graph distances, distance-dependent weight matrices, and weighted sum conversion.
result The proposed method slightly outperforms the original weave module in compound activity prediction.
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learningstat.ML
This paper introduces a new nonlinear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so-called displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary atoms; such at…
Develops conformal Bayes for two-sided censored Gaussian regression under label shift.
problem Prediction under label shift with censored responses.
method Combines posterior predictive tilting with weighted conformal calibration.
result Restores marginal coverage with smaller prediction sets.
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training …
Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary. We consider an extension of this framework where the atoms are further assumed to be embedded in a tree. This is achieved using a recently introduced tree-structured sparse regularization norm, which has pr…
In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as "atoms" allow us to define "atomic norms" that can be used to formulate convex regularizations for the reconstruction problem. …
The paper introduces a new screening method for faster L1 regularization.
problem Efficiently solving the -regularized least squares problem.
method Combining safe screening tests and structured dictionary approximations.
result Significant reductions in computational complexity and execution times.
Proposes an algorithm for infinite-dimensional sparse learning in system identification.
problem System identification without known model structures.
method Atomic norm regularization and greedy algorithm for solving an infinite-dimensional group lasso problem.
result The proposed algorithm outperforms benchmark methods in impulse response fitting and pole location estimation.
This work proposes a scalable framework for trusted multi-party computations using blockchain.
problem Ensuring trust in results from multi-agent computational experiments.
method Combining distributed validation and blockchain for immutable audits, reducing storage and communication costs.
result Guaranteed verifiability and validity of local computations in a scalable multi-agent environment.
Generative models encode and decode 3D crystal structures from a large dataset.
problem Challenges in encoding and decoding 3D crystal structures from large datasets.
method Training two neural networks on a dataset of over 120,000 crystal structures to encode and decode 3D atom positions.
result Ability to generate compressed, continuous latent space representations and decode molecules accurately.
Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision, genome study and medical imaging. One important issue in applying sparse representation is to find…
Learning molecular energies using localized graph kernelsphysics.comp-ph
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab-initio calculations) and at speeds suitable for molecular dynam- ics simulation. Best performance is achieved when the known physical constraints are encoded in the mac…
Robust method for finding sparse solutions to linear inverse problems using an L2 regularizationmath.NA
We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from an overcomplete dictionary. By changing the geometry of the estimation problem, CPA gives an analyti…
The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio …