# Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

arXiv: https://arxiv.org/abs/2111.01905 · HTML: https://mlsanity.com/paper/2111.01905/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 82 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Deep neural network solution of the electronic Schr"odinger equation](https://mlsanity.com/paper/1909.08423/) (2019)
- [Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions](https://mlsanity.com/paper/1906.10033/) (2019)
- [By-passing the Kohn-Sham equations with machine learning](https://mlsanity.com/paper/1609.02815/) (2016)
- [Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics](https://mlsanity.com/paper/2009.08551/) (2020)
- [Learning the exchange-correlation functional from nature with fully differentiable density functional theory](https://mlsanity.com/paper/2102.04229/) (2021)
- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)
- [Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks](https://mlsanity.com/paper/2106.13327/) (2021)
- [Big-Data Science in Porous Materials: Materials Genomics and Machine Learning](https://mlsanity.com/paper/2001.06728/) (2020)
- [Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning](https://mlsanity.com/paper/2011.07923/) (2020)
- [MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks](https://mlsanity.com/paper/2109.08098/) (2021)
- [A Two-stage Framework for Compound Figure Separation](https://mlsanity.com/paper/2101.09903/) (2021)
- [EXSCLAIM! -- An automated pipeline for the construction of labeled materials imaging datasets from literature](https://mlsanity.com/paper/2103.10631/) (2021)
- [Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles](https://mlsanity.com/paper/2106.13109/) (2021)
- [Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning](https://mlsanity.com/paper/2107.14280/) (2021)

## Built on by (most cited first, 10 of 10)

- [Unsupervised Keypoints from Pretrained Diffusion Models](https://mlsanity.com/paper/2312.00065/) (2023)
- [A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models](https://mlsanity.com/paper/2210.14191/) (2022)
- [Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem](https://mlsanity.com/paper/2301.08441/) (2023)
- [A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations](https://mlsanity.com/paper/2208.01692/) (2022)
- [MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science](https://mlsanity.com/paper/2606.07712/) (2026)
- [Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel](https://mlsanity.com/paper/2208.07612/) (2022)
- [Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics](https://mlsanity.com/paper/2407.00671/) (2024)
- [Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis](https://mlsanity.com/paper/2503.04870/) (2025)
- [Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints](https://mlsanity.com/paper/2503.21095/) (2025)
- [Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators](https://mlsanity.com/paper/2605.09495/) (2026)

## Abstract (arXiv)

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure-property relationships from the literature. Models trained on these data sets will improve as they incorporate community feedback.