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Results for tensor-valued diffusion encoding

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8 papers · ranked by Valyu relevance

01
CchemRxiv4 Oct 2024
From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
63relevance
02
CchemRxiv28 Aug 2024
Defect diffusion graph neural networks for materials discovery in high-temperature, clean energy applications
Lauren Way, Catalin Spataru, Reese Jones, Dallas Trinkle + 10 more
The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they…
61relevance
03
CchemRxiv17 Jan 2025
AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics
Authors not listed
Elucidating Collective Variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to…
60relevance
04
CchemRxiv8 Nov 2022
Generative adversarial networks and diffusion models in material discovery
Michael Alverson, Sterling Baird, Ryan Murdock, Taylor Sparks
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal…
56relevance
05
CchemRxiv26 Oct 2022
Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks
Maxwell Venetos, Mingjian Wen, Kristin Persson
The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Re- cently, machine learning has been applied to NMR in the prediction of isotropic chemi- cal shifts from a structure. Current machine learning models…
56relevance
06
CchemRxiv26 Apr 2024
Performance metrics for tensorial learning: prediction of Li4Ti5O12 nuclear magnetic resonance observables at experimental accuracy
Angela F Harper, Simone S Köcher, Karsten Reuter, Christoph Scheurer
Machine learning (ML) surrogate modeling is a powerful approach to reduce the computational cost of first-principles calculations. While well established for the prediction of scalar observables like energetics or band gaps, performance metrics for the learning of tensor-based observables have not yet been formalized.…
55relevance
07
CchemRxiv30 Apr 2024
Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
54relevance
08
CchemRxiv2 Oct 2025
Pharmacophore model guided 3D molecular generation through diffusion model
Authors not listed
Generating molecules that rigorously satisfy three-dimensional (3D) pharmacophore constraints has remained a challenge in de novo drug design, despite rapid progress in molecular generative models. We present DiffPharm, a diffusion-based framework that embeds explicit pharmacophore control into the generative process.…
52relevance

Results for tensor-valued diffusion encoding

From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics

Defect diffusion graph neural networks for materials discovery in high-temperature, clean energy applications

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics

Generative adversarial networks and diffusion models in material discovery

Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks

Performance metrics for tensorial learning: prediction of Li4Ti5O12 nuclear magnetic resonance observables at experimental accuracy

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design

Pharmacophore model guided 3D molecular generation through diffusion model

From Clutter to Clarity: Emergent Neural Operators via Questionnaire Metrics

Defect diffusion graph neural networks for materials discovery in high-temperature, clean energy applications

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics

Generative adversarial networks and diffusion models in material discovery

Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks

Performance metrics for tensorial learning: prediction of Li4Ti5O12 nuclear magnetic resonance observables at experimental accuracy

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design

Pharmacophore model guided 3D molecular generation through diffusion model