ReviewJournal of the American Chemical Society2022
Into the Unknown: How Computation Can Help Explore Uncharted Material Space.
Review in Journal of the American Chemical Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Unbiased Structure Prediction of Sophisticated Cage Structures.Angewandte Chemie (International ed. in English) · 2026Article
- A Problem-Setup-Centric Framework for Mixed-Variable Chemical Formulation Optimization with Customized Constraints.Materials (Basel, Switzerland) · 2026Article
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.Journal of chemical theory and computation · 2026Article
- Architecting Functional Polymers: Advances in Modular Synthesis, Responsive Design, and Multifaceted Applications.Polymers · 2026Review
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Unveiling Unusual Reactivity of SOInorganic chemistry · 2025Article
- Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks.JACS Au · 2025Article
- Chemical space as a unifying theme for chemistry.Journal of cheminformatics · 2025Article
- Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages.Beilstein journal of organic chemistry · 2025Article
- Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models.PeerJ. Computer science · 2025Article
- Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning.JACS Au · 2024Article
- Biomimetic Frustrated Lewis Pair Catalysts for Hydrogenation of CO to Methanol at Low Temperatures.ACS organic & inorganic Au · 2024Article
- Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory.Nature communications · 2024Article
- Confining He Atoms in Diverse Ice-Phases: Examining the Stability of He Hydrate Crystals through DFT Approaches.Molecules (Basel, Switzerland) · 2023Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier.Journal of the American Chemical Society · 2023Article
- Generative Models as an Emerging Paradigm in the Chemical Sciences.Journal of the American Chemical Society · 2023Review
- Experimental Studies on TiOMaterials (Basel, Switzerland) · 2023Review
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
Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.
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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.