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.
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
Optimized DMD for fast atmospheric chemistry forecasting.
Deep QMC methods use neural networks to solve quantum chemistry problems.
Derives symmetric and antisymmetric kernels for quantum physics and chemistry applications.
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the la…
BERT learns molecular substructures for chemistry problems.
New dataset DFT for drug-like molecules benchmarks neural network potentials.
Accelerated RPCholesky speeds up kernel matrix approximations.
Review of automation's role in chemical discoveries.
We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works at the level of individual (artificial) molecules, subject of reversible, but otherwise determinist…
Steerable E(3) Graph Neural Networks incorporate geometric and physical covariant information.
Zipper logic is a graph rewrite system, consisting in only local rewrites on a class of zipper graphs. Connections with the chemlambda artificial chemistry and with knot diagrammatics based computation are explored in the article.
We compute Khovanov homology for tangles using TQFT.
This work improves chemistry modeling by jointly learning reaction progress variables and look-up models.
In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the predict…
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely o…
Study examines how taxes affect wealth inequality in economic models.
New model accurately predicts chemical bond breaking.
Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research,…
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…
Graphs predict reaction conditions for organic chemistry.
BoFire optimizes chemistry experiments using Bayesian Optimization.
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential…
ML-FFs use ML to bridge chem. accuracy and efficiency.
New algorithm efficiently trains machine learning models to atomic forces data.
A new training method improves MLIPs for faster, lighter simulations.
A new method relaxes molecules without needing non-equilibrium data.
A new model explains protein interactions via electron delocalization.
We study the problem of determining the least symmetric triangle, which arises both from pure geometry and from the study of molecular chirality in chemistry. Using the correspondence between planar -gons and points in the Grassmannian of 2-planes in real -space introduced by Hausmann and Knutson, this correspond…
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy…
Quantum machine learning boosts drug discovery efficiency.
Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.
We propose a hybrid quantum-classical algorithm, originated from quantum chemistry, to price European and Asian options in the Black-Scholes model. Our approach is based on the equivalence between the pricing partial differential equation and the Schrodinger equation in imaginary time. We devise a strategy to build a s…
CRPS improves GP-based sequential design for chemical space.
Olympus benchmarks optimization algorithms for noisy experiments.
Deep neural network predicts molecular wave functions in minimal basis.
A new approach to quantum machine learning circuits reduces training difficulties.
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built…
Machine learning aids excited-state molecular dynamics studies.
Retro* uses neural networks to efficiently find high-quality synthetic routes in organic chemistry.
FlowMM models stable crystal structures efficiently.
ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data pr…
Deep learning improves OFDFT for molecular systems.
GDB bridges geometric states with improved accuracy and generality.
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such …
We present a framework, which we call Molecule Deep -Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double -learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100…
Neural network (NN) model chemistries (MCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and…