# Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

arXiv: https://arxiv.org/abs/1803.11246 · HTML: https://mlsanity.com/paper/1803.11246/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 11 of 11)

- [Bayesian optimization with known experimental and design constraints for chemistry applications](https://mlsanity.com/paper/2203.17241/) (2022)
- [Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models](https://mlsanity.com/paper/2312.05472/) (2023)
- [Artificial Intelligence and Generative Models for Materials Discovery -- A Review](https://mlsanity.com/paper/2508.03278/) (2025)
- [Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation](https://mlsanity.com/paper/2103.03391/) (2021)
- [Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop Annotations](https://mlsanity.com/paper/2408.12720/) (2024)
- [Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models](https://mlsanity.com/paper/2108.13171/) (2021)
- [Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up](https://mlsanity.com/paper/2110.03923/) (2021)
- [Scientific Computing with Diffractive Optical Neural Networks](https://mlsanity.com/paper/2302.10905/) (2023)
- [Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer](https://mlsanity.com/paper/2402.13353/) (2024)
- [Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data](https://mlsanity.com/paper/2412.17283/) (2024)
- [SemiConLens: Visual Analytics for 2D Semiconductor Discovery](https://mlsanity.com/paper/2605.04067/) (2026)

## Abstract (arXiv)

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.