Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores

• URL: http://arxiv.org/abs/2512.01080v1
• Date: Sun, 30 Nov 2025 21:02:00 GMT
• Title: Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
• Authors: Benhour Amirian, Ashley S. Dale, Sergei Kalinin, Jason Hattrick-Simpers,
• Abstract summary: Key challenge in using AI is ensuring that human scientists trust the models are valid and reliable.<n>We evaluate whether reported machine learning methods are generalizable, interpretable, fair, transparent, explainable, robust, and stable.
• Score: 0.09999629695552194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human scientists trust the models are valid and reliable. Accordingly, we define a trustworthy AI framework GIFTERS for materials science and discovery to evaluate whether reported machine learning methods are generalizable, interpretable, fair, transparent, explainable, robust, and stable. Through a critical literature review, we highlight that these are the trustworthiness principles most valued by the materials discovery community. However, we also find that comprehensive approaches to trustworthiness are rarely reported; this is quantified by a median GIFTERS score of 5/7. We observe that Bayesian studies frequently omit fair data practices, while non-Bayesian studies most frequently omit interpretability. Finally, we identify approaches for improving trustworthiness methods in artificial intelligence and machine learning for materials science by considering work accomplished in other scientific disciplines such as healthcare, climate science, and natural language processing with an emphasis on methods that may transfer to materials discovery experiments. By combining these observations, we highlight the necessity of human-in-the-loop, and integrated approaches to bridge the gap between trustworthiness and uncertainty quantification for future directions of materials science research. This ensures that AI/ML methods not only accelerate discovery, but also meet ethical and scientific norms established by the materials discovery community. This work provides a road map for developing trustworthy artificial intelligence systems that will accurately and confidently enable material discovery.

Related papers

• Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval [60.25608870901428]
  Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs)<n>We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source robustness.
  arXiv  Detail & Related papers  (2026-03-05T18:42:51Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration [63.61423859450929]
  This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses.<n>We identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery.
  arXiv  Detail & Related papers  (2026-01-20T18:46:42Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• The Need for Verification in AI-Driven Scientific Discovery [9.887965168376311]
  Machine learning and large language models can generate hypotheses at a scale and speed far exceeding traditional methods.<n>We argue that without scalable and reliable mechanisms for verification, scientific progress risks being hindered rather than being advanced.
  arXiv  Detail & Related papers  (2025-09-01T11:50:04Z)
• Artificial Intelligence and Generative Models for Materials Discovery -- A Review [0.0]
  Review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery.<n>We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals.
  arXiv  Detail & Related papers  (2025-08-05T09:56:27Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• Is Trust Correlated With Explainability in AI? A Meta-Analysis [0.0]
  We conduct a comprehensive examination of the existing literature to explore the relationship between AI explainability and trust.<n>Our analysis, incorporating data from 90 studies, reveals a statistically significant but moderate positive correlation between the explainability of AI systems and the trust they engender.<n>This research highlights its broader socio-technical implications, particularly in promoting accountability and fostering user trust in critical domains such as healthcare and justice.
  arXiv  Detail & Related papers  (2025-04-16T23:30:55Z)
• PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration [0.0]
  We introduce principles-guided material discovery system powered by language inferential multi-agent system (MAS)<n>Our framework integrates automated hypothesis generation with experimental validation in a roundtable system of MAS.<n>Based on our framework, the case study of nano helix demonstrates higher materials exploration rate and property value.
  arXiv  Detail & Related papers  (2025-04-09T03:05:10Z)
• Agent-based Learning of Materials Datasets from Scientific Literature [0.0]
  We develop a chemist AI agent, powered by large language models (LLMs), to create structured datasets from natural language text. Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles.
  arXiv  Detail & Related papers  (2023-12-18T20:29:58Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI [49.64037266892634]
  We describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is twofold: first, to provide a broad range of capabilities to streamline as well as foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle; second, to encourage further exploration of UQ's connections to other pillars of trustworthy AI.
  arXiv  Detail & Related papers  (2021-06-02T18:29:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.