A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study evaluates uncertainty quantification for atomistic neural networks, revealing complex relationships between error and uncertainty.
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of …
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule…
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not …
New model accurately predicts chemical bond breaking.
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is re…
Novel RL approach for molecular design using quantum mechanics.
Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simult…
Hyperbolic volume correlates with chemical properties of fullerenes.
Deep neural network predicts molecular wave functions in minimal basis.
Machine learning speeds up quantum chemical calculations of excited states.
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine l…
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
ML-FFs use ML to bridge chem. accuracy and efficiency.
Efficient memory layer improves graph neural networks for graph classification and regression.
Generative neural network designs novel 3D molecules with specified properties.
This paper speeds up simulations of hypersonic reentry by combining traditional and neural methods.
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics. Machine learning holds the promise of learning the kinetic energy functional …
Machine learning models simulate molecular spectra and reactions in solvents.
This review discusses challenges and solutions for AI in chemical engineering.
The paper develops methods to assess and correct model uncertainties in graphical models.
Machine learning employs dynamical algorithms that mimic the human capacity to learn, where the reinforcement learning ones are among the most similar to humans in this respect. On the other hand, adaptability is an essential aspect to perform any task efficiently in a changing environment, and it is fundamental for ma…
Quantum CNNs improve on multi-channel data processing.
Quantum federated learning improves with non-IID data using one-shot communication.
New method finds graphene nanocrystals with reduced DFT calculations.
We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the un…
Machine learning aids excited-state molecular dynamics studies.
This work explores using deep NNs to learn quantum systems from probability distributions.
The paper develops a Gaussian process model for predicting chemical efficacy.
CRNN discovers chemical reaction pathways from data.
Post-quantum cryptography needed for blockchain security.
Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a…
Quantum machine learning improves pulsar classification in radio astronomy.
Machine learning predicts liquid water properties from cluster data.
New dataset DFT for drug-like molecules benchmarks neural network potentials.
Paper explores ML for UV spectra, showing transferability in chemical space.
The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…
New model predicts molecular wavefunctions and densities with unprecedented accuracy.
Quantum machine learning faces challenges similar to variational quantum algorithms in training.
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynam…
Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict mat…
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a…
A new graph model HMG and neural network HMGNN improve molecule property predictions.
Paper proposes COM-QEL to avoid overoptimistic solutions in offline optimization.
New method uses single quantum state for machine learning tasks, improving accuracy.
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep …
Quantum polynomials are derived from a specific tribracket structure.
The quantum differential equations can be regarded as examples of equations with certain universal properties which are of wider interest beyond quantum cohomology itself. We present this point of view as part of a framework which accommodates the KdV equation and other well known integrable systems. In the case of qua…