Unified machine learning predicts molecular wavefunctions efficiently.
problem Lack of explicit electronic structure in machine learning models for chemistry.
method Deep neural network for quantum mechanical wavefunction prediction.
result Efficient prediction of molecular wavefunctions with full electronic structure access.
FAT-GAN simulates electron-proton scattering without theoretical assumptions.
problem Efficiently training GANs to simulate complex particle distributions.
method Developed FAT-GAN using transformed and augmented features to improve GAN performance.
result FAT-GAN accurately reproduces electron momenta distributions in electron-proton scattering.
Optimal market making strategy for electronic markets with persistent order flows.
problem Market making on electronic markets with persistent order flows.
method Formulated as a stochastic control problem, characterized by viscosity solutions, and implemented numerically.
result Characterization of an optimal market making strategy.
New model predicts molecular wavefunctions and densities with unprecedented accuracy.
problem Challenging task of predicting wavefunctions due to molecular rotations.
method Introduces SE(3)-equivariant operations for deep learning.
result Achieves speedups and error reductions over ab initio methods.
Develops machine learning models for excited states of CH2NH2+.
problem Accurately predicting excited-state properties and couplings for CH2NH2+.
method Combines neural networks and kernel ridge regression, encoding electronic states in inputs.
result Improved accuracy in predicting excited-state properties and couplings.
Machine learning predicts electronic density of states for condensed matter.
problem Predicting the electronic density of states (DOS) in complex condensed matter systems.
method Developed a machine learning framework to predict DOS from density functional theory data, considering geometric configurations of atoms.
result Demonstrated the model's effectiveness in predicting DOS and its components for various silicon configurations.
Deep learning wave function improves quantum chemistry calculations.
problem Solving the electronic Schrödinger equation for complex molecules is computationally expensive.
method PauliNet, a deep learning wave function ansatz that incorporates physics and is trained with VMC.
result PauliNet achieves nearly exact solutions and outperforms other methods for various molecules.
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.
ARX models predict thermal behavior of WBG semiconductors accurately.
problem Thermal management challenges of WBG semiconductors.
method Use of ARX parametric models based on experimental measurements.
result ARX models provide accurate temperature predictions without detailed component information.
Data-driven approach discovers molecular photoswitches with separated electronic absorption bands.
problem Engineering photoswitchable molecules with specific electronic absorption bands remains challenging.
method Data-driven discovery pipeline using Gaussian processes for multitask learning.
result Multioutput Gaussian process (MOGP) trained on four photoswitch transition wavelengths outperforms single-task models and TD-DFT.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
problem Estimating optimal treatment regimes from electronic medical records.
method Imputation-based semi-supervised method using unlabeled data.
result Proposed method yields more efficient estimators of optimal treatment regimes.
Study on electronic banking satisfaction in Nigeria.
problem Limited research on factors enhancing end users' satisfaction in electronic banking.
method Empirical analysis of factors influencing electronic banking user satisfaction.
result Factors influencing electronic banking user satisfaction and their relationship with satisfaction.
Libra ensures fair order-matching in electronic financial exchanges.
problem Technical shortcomings and infrastructure complexities in electronic trading.
method Formally defined temporal fairness, evaluated existing fair market designs, introduced Libra.
result Libra is more robust and resilient to technical manipulation than existing designs.
Deep neural network predicts molecular wave functions in minimal basis.
problem Improving accuracy and efficiency in quantum chemistry calculations.
method Adapted SchNet for Orbitals (SchNOrb) model in quasi-atomic minimal basis.
result Model accurately predicts molecular orbital energies and wavefunctions for large molecules.
New method generates diverse EHR data types while maintaining privacy.
problem Privacy risks in sharing EHRs across large scales.
method Refined GAN model, feature constraints, and utility measures.
result New model preserves EHR statistics, correlations, and constraints.
Framework learns inter-electronic potential for molecular dynamics.
problem Predicting time-dependent Hartree-Fock dynamics from electron density.
method Developed three models using four-index tensors, preserving symmetries.
result Model with eight-fold symmetry performs best across metrics.
Machine learning improves molecular dynamics simulations by reducing costs and enhancing accuracy.
problem Inaccurate and costly molecular dynamics simulations hinder chemical system description.
method Adaptive sampling of reference data points and machine learning models for predicting molecular properties.
result Machine learning models can predict molecular dipole moments and infrared spectra accurately.
Method learns molecular Hamiltonian for accurate electron dynamics predictions.
problem Predict electron dynamics in molecules using learned Hamiltonians.
method Combines linear statistical model with quantum Liouville equation time discretization.
result Predicted electron dynamics closely matches ground truth, even beyond training data.
DenSNet learns electron densities for molecular dynamics, enabling accurate spectroscopic predictions.
problem Lack of accurate electronic observables in MLIPs for molecular dynamics.
method DenSNet uses SE(3)-equivariant neural networks to predict electron densities and total energy.
result DenSNet predicts infrared spectra with excellent agreement to experimental data.
Deep QMC method accurately computes electronic excited states.
problem Accurate calculation of electronic excited states in large systems.
method Extends variational QMC with deep neural networks for excited states.
result Consistently achieves high accuracy for low-lying excited states.
Machine learning predicts band gaps for large organic crystals.
problem Predicting band gaps for complex organic crystal structures.
method Released a dataset of 12,500 crystal structures and their band gaps. Trained two state-of-the-art models to achieve a mean absolute error of 0.388 eV.
result Trained models predict band gaps with 13% error for an average gap of 3.05 eV.
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for di…
Agent-based model simulates speculative electronic market with price bubbles.
problem Understanding speculative behavior and price bubbles in electronic markets.
method Agent-based model with two types of traders: mean reverting and speculative.
result Speculative traders lead to increased volatility and price deviations from fundamental value.
Study examines how twisting graphene nanoribbons affects their thermal conductivity.
problem Understanding how twisting affects thermal conductivity in graphene nanoribbons.
method Calculated geometric parameters of TGNRs, including twist and writhe, and used molecular dynamics simulations.
result Twisted graphene nanoribbons require at least two parameters to accurately describe their thermal conductivity.
Automated VOC classification using embedded machine learning.
problem Classifying volatile organic compounds (VOCs) from electronic nose data.
method Embedded machine learning algorithm on Arduino Uno for VOC detection.
result 99% classification accuracy achieved in embedded solution.
Prototype for adaptive electron microscopy scans reduces dose and time.
problem Reduce electron microscopy scan time and dose with minimal loss.
method Adaptive partial scanning with reinforcement learning.
result Reinforcement learning trained neural network optimizes scan paths.
Generative neural network designs novel 3D molecules with specified properties.
problem Designing molecules with desired properties in chemistry.
method Conditional generative neural network for 3D molecular structures.
result Demonstrated utility in generating novel molecules with specified motifs or composition.
High Frequency Trading (HFT) represents an ever growing proportion of all financial transactions as most markets have now switched to electronic order book systems. The main goal of the paper is to propose continuous time equations which generalize the self-financing relationships of frictionless markets to electronic …
Deep Q-Learning method for Nash equilibria in stochastic games.
problem Model-free learning for multi-agent stochastic games, especially for general-sum games.
method Data-efficient Deep-Q-learning using local linear-quadratic expansion parametrized by deep neural networks.
result The algorithm learns optimal actions for stochastic games without experiencing all state-action pairs.
A multi-scale model predicts atomic-scale properties using both local and long-range information.
problem Inability of machine-learning schemes to capture long-range physical effects.
method Combines local and non-local information in a multipole expansion framework.
result Demonstrates the ability to model electrostatics, polarization, and dispersion.
Embeddings of lab test codes improve mortality prediction and preserve ordinality.
problem Improving mortality prediction using lab test embeddings.
method Training embeddings for LOINC codes and their concatenations with abnormality symbols, evaluating performance on mortality prediction tasks.
result Embeddings of lab test codes improve mortality prediction and preserve ordinality.
Equivariant graph neural networks predict electron density for molecules, liquids, and solids.
problem Predicting electron density for molecules, liquids, and solids using machine learning.
method Equivariant graph neural networks for predicting electron density at query points.
result The model predicts electron density with accuracy beyond state of the art and significantly faster than traditional DFT methods.
Neural network predicts electron-ionization mass spectra quickly.
problem Identifying unknown molecules not in existing libraries.
method Lightweight neural network model for predicting mass spectra.
result High accuracy predictions of small molecule mass spectra.
Deep learning speeds up design of organic photovoltaic structures.
problem Designing optimal organic photovoltaic structures is expensive and intractable.
method Introduced a CNN architecture as a fast surrogate for structure-property mapping and used it for robust microstructural design.
result Deep learning accelerates the design process for enhancing photovoltaic device performance.
Machine learning aids excited-state molecular dynamics studies.
problem Challenges in studying electronically excited states of molecules.
method Employing machine learning techniques for excited-state molecular dynamics.
result Highlight successes and challenges in machine learning for excited-state processes.
Paper proposes a method to reduce sensor drift in electronic noses.
problem Sensor drift in electronic noses.
method Discriminative subspace projection approach.
result The method minimizes within-class variance and maximizes between-class variance using label information.
We consider the Dirac equation in flat Minkowski 3-space and rewrite it as the Maxwell equation in Minkowski 4-space with torsion. The torsion tensor is defined as the dual of the electromagnetic vector potential. Our model clearly distinguishes the electron and the positron without resorting to "negative frequencies":…
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeli…
X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to char…
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction …
The paper discusses challenges and solutions in trading using AI.
problem Challenges in data-driven learning for electronic trading.
method Explains neural information processing and machine learning approaches.
result Outlines the challenges and solutions in trading AI.
TeaNet uses GCNs to model complex atomic interactions inspired by electronic relaxation.
problem Creating a universal interatomic potential for all elements.
method Tensor-embedded atom network (TeaNet) using graph convolutional neural networks (GCNs).
result TeaNet achieves good performance (19 meV/atom) for structures and reactions involving elements from H to Ar.
Develops methods to learn correlation potentials for time-dependent Kohn-Sham systems.
problem Learning the correlation potential for time-dependent Kohn-Sham systems.
method Optimizing a least-squares objective subject to the TDKS equation using adjoints.
result Learned correlation potential models match ground truth electron densities and can have memory.
Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials.
problem Predicting magnetic ground states, moments, and anisotropy in two-dimensional magnets.
method Introduce the symmetry-electronic fingerprint (SEF), a physically interpretable representation that encodes crystallographic symmetry operations, Wyckoff-site geometry, and site-resolved electronic structure.
result SEF-trained models accurately classify magnetic ordering and regress moments alongside anisotropy energies.
Method estimates section thickness and XY anisotropy in ssEM images.
problem Accurate 3D reconstructions require precise section thickness and XY anisotropy estimates.
method Non-parametric Bayesian regression of image statistics.
result Method has lower estimation error compared to existing methods.
AHEAD improves financial market efficiency through ad-hoc auctions.
problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.
Paper improves communication in decentralized federated learning for EHRs.
problem Efficiently learn from large, decentralized EHR databases.
method Fully decentralized federated learning with iterative local updates and reduced communication rounds.
result Significant reduction in communication rounds without compromising solution optimality.
Bayesian model predicts patient survival from sparse EHR data.
problem Analyzing EHR data with few samples and diverse information.
method Nonparametric probabilistic model using Bayesian trees.
result Improved survival trajectory predictions on patient data.