ReviewChemical reviews2024
Data Generation for Machine Learning Interatomic Potentials and Beyond.
Review in Chemical reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States.Journal of chemical information and modeling · 2026Article
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Article
- Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.JACS Au · 2026Review
- Mechanochemical disassembly pathways of self-assembled polymer-decorated PdNature communications · 2026Article
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying.Materials (Basel, Switzerland) · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Performance of Meta's Universal Model for Atoms across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts.The journal of physical chemistry. A · 2026Article
- Advancing the Frontiers: Contemporary Achievements and Future Directions in Density Functional Theory Calculations.Molecules (Basel, Switzerland) · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Latest Advancements and Mechanistic Insights into High-Entropy Alloys: Design, Properties and Applications.Materials (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.
Identifiers
What Socratic 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 Socratic graph.