ReviewCommunications chemistry2025
Deciphering the complexities of crystalline state(s) with molecular simulations.
Review in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rapid Exploration of Mixture Adsorption via Adiabatic Sampling.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science.The journal of physical chemistry letters · 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
2 authors.
Funding
Abstract
Predicting the outcome of a crystallization process remains a long-standing challenge in solid state chemistry. This stems from the subtle interplay between thermodynamics and kinetics that results in a complex crystal energy landscape, spanned by many polymorphs and other metastable intermediates. Molecular simulations are uniquely positioned to unravel this interplay, as they constitute a framework that can compute free energies (thermodynamics), barriers (kinetics), and visualize the crystallization mechanisms at high resolution. We show here how recent progress in computational methods, and their augmentation with Machine Learning, has advanced our ability to predict crystal structure and simulate crystal nucleation.
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.