Materials Discovery

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29 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
Oct 13, 2026Yevgeny Rakita, Paul Todd, James R. Neilson +1Materials Discovery Materials Science
Department of Materials Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel · Department of Chemistry, Colorado State University, Fort Collins, CO 80523 · Department Materials, University of California, Santa Barbara, Santa Barbara, CA 93106
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design
Oct 13, 2026Yu Zeng, Yuchuan Chen, Ziyue Zhang +5Materials Discovery Bayesian Optimization
DP Technology, Beĳing, China
OxiGen: Oxidation-State-Aware Crystal Generation
Oct 6, 2026Dylan John, Kim E. Jelfs, Alex M. Ganose +1Constrained Generative Modeling Crystal Structure Generation
Department of Chemistry, Imperial College London · Department of Electrical and Electronic Engineering, Imperial College London
CrystalJev: calibrated foundation-model probabilities decide what to compute next in materials discovery
Oct 4, 2026Peng Kang, Yaxuan Sun, Zhen Li +3Materials Discovery Materials Property Prediction
School of Materials Science and Engineering, Beihang University, Beijing 100191, China · National Key Laboratory of Artificial Intelligence for Material Science, Beihang University, Beijing 100191, China
Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
Oct 2, 2026Robert Robinson, Shakti Prasad Padhy, Sushant Sinha +6Materials Discovery Bayesian Optimization
Department of Materials Science and Engineering, Texas A&M University, College Station, 77843, TX, USA · J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, 77843, TX, USA · Wm Michael Barnes ’64 Department of Industrial and Systems Engineering, College Station, 77843, TX, USA
OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery
Sep 22, 2026Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor +1Flow Matching Crystal Structure Prediction
New York University · University of Minnesota
A literature-guided descriptor-based framework for filtering composition search spaces
Sep 14, 2026Lei Zhang, Markus StrickerMaterials Discovery Computational Materials Science
Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, North Rhine-Westphalia, Germany
SCALE: Simulation-Calibrated Amortized Learning for Energy Materials (A hybrid architecture connecting deterministic modeling, real-world data, and transformer-scale inference for accelerated energy-materials discovery)
Sep 3, 2026Kuan Huang, Bo BaiMaterials Discovery Materials Property Prediction
Alpha Ladder Particle Pte. Ltd., 168 Robinson Road, #26-02, Singapore 068912
Learning Materials Properties from Scarce Labels and Unlabeled Crystals
Aug 31, 2026Wentao Li, Yizhe Chen, Jiangjie Qiu +3Materials Property Prediction Pseudo-Labeling
1Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials, State Key Laboratory of Chemical Engineering and Low-Carbon Technology, Department of Chemical Engineering, Tsinghua University
Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design
Aug 28, 2026Chuhan Yang, Chenxi Wang, Linhan Wu +1Materials Discovery Inverse Design
DeepVerse
An LLM agent for end-to-end computational materials discovery
Aug 20, 2026Chen Yuntong, Huang Ju, Liu Yu +6Materials Discovery AI Agents for Scientific Discovery
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China. · Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, M5S 3E5, ON, Canada. · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100045, China. +2
Crystal-structure design by agentic AI in a language of motifs
Aug 16, 2026Dinh-Khiet Le, Minh-Quyet Ha, Hong-Phuc Vu-Dinh +3Crystal Structure Generation Materials Discovery
Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan · Department of Physics, School of Science, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan · Research Center for Materials Informatics, Department of Advanced Data Science, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan +1
Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery
Aug 5, 2026Alexandros Ntagiantas, Panagiotis Tsilimidos, George Giannakopoulos +2Bayesian Optimization Active Learning
National Centre for Scientific Research “Demokritos”
Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery
Jul 26, 2026Debajyoti Ray, Niranjan SrinivasCost-Sensitive Learning Bayesian Optimization
Coactive Science
From Materials Database to Materials Bank: Assetizing Data for AI Driven Materials Innovation
Jun 30, 2026Chenyao Ma, Di Zhang, Weibo Gong +9AI-Assisted Decision Making Materials Discovery
Suzhou MatSource Technology Co., Ltd., Suzhou 215000, Jiangsu, China. · Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan · Frontier Research Institute for Interdisciplinary Sciences (FRIS), Tohoku University, Sendai, 980-8577, Japan +2
Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
Jun 26, 2026Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez +2Surrogate-Assisted Optimization Bayesian Optimization
Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA · Theoretical Division T-1, Los Alamos National Laboratory, Los Alamos, NM 87544, USA · Max-Planck-Institute for Sustainable Materials, Düsseldorf, Germany 40237 +3
SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery
Jun 21, 2026Bin CaoRL Benchmarks Crystal Structure Generation
The Hong Kong University of Science and Technology (Guangzhou), China · Huawei Noah’s Ark Lab, UK
InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery
Jun 15, 2026Wen-Kao Li, Ze-Feng Gao, Peng-Jie Guo +2Materials Property Prediction Autonomous Scientific Discovery
School of Physics and Key Laboratory of Quantum State Construction (Ministry of Education), Renmin University of China, Beijing 100872, China
NIMO: A Software Platform for Closed-Loop Materials Exploration with Diverse AI Algorithms
Jun 14, 2026Ryo Tamura, Naruki Yoshikawa, Koji Tsuda +1Materials Discovery Materials Science
Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan · Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan · Center for Green Research on Energy and Environmental Materials, National Institute for Material Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
May 18, 2026Chih-Yu Lee, Haotong Liang, Ryan Kim +4Materials Science Autonomous Scientific Discovery
Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA · Materials Measurement Science, Division of the National Institute of Standards and Technology, Gaithersburg, MD, USA · Institute of Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland, USA +1
PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation
May 15, 2026Claire Schlesinger, Circe Hsu, Peter Schindler +1Materials Science Crystal Structure Generation
Khoury College of Computer Sciences Northeastern University Boston, MA 02115 · College of Engineering Northeastern University Boston, MA 02115
Composable Crystals: Controllable Materials Discovery via Concept Learning
May 14, 2026Nian Liu, Yuwei Zeng, Ryoji Kubo +7Vector-Quantized Variational Autoencoder Concept Learning
National University of Singapore · Loyola Marymount University
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
May 14, 2026Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8Joint-Embedding Predictive Architecture Generative Modeling
1National University of Singapore · 2Loyola Marymount University · 3New York University +1
Expanding the extreme-k dielectric materials space through physics-validated generative reasoning
Apr 22, 2026Hossain Hridoy, Tahiya Chowdhury, Md Shafayat HossainMaterials Science Large Language Model-Guided Scientific Discovery
Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. · Department of Computer Science, Colby College, Waterville, ME, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +2
LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation
Mar 16, 2026AI Scientists, Xinyi Lin, Danqing Yin +1Materials Discovery Materials Science
School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, 999077, China · Laboratory of Data Discovery for Health Limited (D24H), Pak Shek Kok, Hong Kong SAR, 999077, China · Room 312, Lau Chung Him Building, 8 Castle Peak Road, Tuen Mun, Hong Kong
Offline Materials Optimization with CliqueFlowmer
Mar 6, 2026Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1Materials Discovery Computational Materials Science
BAIR, UC Berkeley, Berkeley, California, USA · FAIR, Meta, San Francisco, California, USA
Clever Materials: When Models Identify Good Materials for the Wrong Reasons
Feb 18, 2026Kevin Maik JablonkaSpurious Correlation Materials Property Prediction
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstrasse 10, 07743 Jena, Germany · Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena), Lessingstrasse 12-14, 07743 Jena, Germany · Center for Energy and Environmental Chemistry Jena (CEEC Jena), Friedrich Schiller University Jena, Philosophenweg 7a, 07743 Jena, Germany +1
Synthesizability Prediction of Crystalline Structures with Structure-Aware Feature Learning and Uncertainty Quantification
Oct 22, 2025Danial Ebrahimzadeh, Sarah Sharif, Yaser Mike BanadMaterials Property Prediction Materials Science
School of Electrical and Computer Engineering, University of Oklahoma, 110 W. Boyd St., Norman, 73019, Oklahoma, USA.
BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search
Jun 5, 2024Wei-Ting Tang, Ankush Chakrabarty, Joel A. PaulsonNovelty Search Black-Box Optimization
University of Wisconsin-Madison, Department of Chemical and Biological Engineering, Madison, WI, USA. · The Ohio State University, Department of Chemical and Biomolecular Engineering, Columbus, OH, USA. · Insulet Corporation, Acton, MA, USA.

Oct 13, 2026J/K move · Enter open · S save

Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration

Yevgeny Rakita, Paul Todd, James R. Neilson, Simon J. L. Billinge

Department of Materials Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel · Department of Chemistry, Colorado State University, Fort Collins, CO 80523 · Department Materials, University of California, Santa Barbara, Santa Barbara, CA 93106

Materials Discovery Materials Science Data Provenance KG Construction

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This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize technologically useful materials, especially those that are kinetically stabilized. Often, it is a science that follows years of trial and error, culminating in (published) successful recipes, but is truly driven by far more (unpublished) failures. The entry into the era of AI provides an opportunity to use learning algorithms to encode rules from multi-dimensional synthesis pathways, regardless of their technological usefulness, and to decode both forward prediction and inverse learning modes. The main incentive is to accelerate material discovery by generating a global knowledge base that can be shared among data agents and contributed to by them. We will focus on several scenarios in the common synthesis process during solid-state synthesis, primarily because these reactions are kinetic in nature and require closer monitoring and control of the reaction history.