ReviewACS physical chemistry Au2024
The Potential of Neural Network Potentials.
Review in ACS physical chemistry Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Supramolecular Host-Guest Complexation Dynamics by Cost-Efficient Electronic Structure Methods.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- ASH: A Multi-Scale, Multi-Theory Modeling Program.Journal of computational chemistry · 2026Article
- Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis.ACS catalysis · 2026Review
- Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.ACS central science · 2026Review
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning.The journal of physical chemistry letters · 2025Article
- Comparative Study of UMCM-9 Polymorphs: Structural, Dynamic, and Hydrogen Storage Properties via Atomistic Simulations.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- On the emergence of machine-learning methods in bottom-up coarse-graining.Current opinion in structural biology · 2025Review
- Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold's black box.Frontiers in molecular biosciences · 2025Article
- Construction and Experimental Validation of Embedded Potential Functions for Ta-Re Alloys.Molecules (Basel, Switzerland) · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.
Identifiers
What OpenQuestion holds
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.