ArticleChemical science2018
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
89 citing papers in PubMed.
- Bridging data and theory: hybrid modelling for pharmaceutical crystallisation process development.Digital discovery · 2026Review
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Learning potential energy surfaces of hydrogen atom transfer reactions in peptides.Digital discovery · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- Atom-scale contributions to nanofriction response in transition metal dichalcogenides.Beilstein journal of nanotechnology · 2026Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges.Chemical science · 2025Review
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
- Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition.ACS electrochemistry · 2025Review
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds.The journal of physical chemistry. A · 2025Article
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.The journal of physical chemistry letters · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy.Chemical science · 2024Article
- Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration.Journal of chemical theory and computation · 2024Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.