Machine Learning in Materials Science

Latest papers

• Latent JEPA: Abstract Future Prediction for Latent Reasoning in Chemistry

  Xinjian Zhao, Yaoyao Xu, Xuemin Chen, Xiaozhuang Song, Tianshu Yu · 2 October 2026

  Large language models offer a promising foundation for chemical reasoning, bringing together chemical knowledge and multistep problem solving. Chemical intuition can provide an initial sense of plausible outcomes before the details of a solution are fully worked out. Inspired by how such expectation…

• CompMat-Bench: Benchmarking AI Agents for Computational Materials Science

  Chenmu Zhang, Levi Felix, Jun-Jie Zhang, Xingfu Li, Xuelian Jiang, Tao Jiang, Subhendu Mishra, Xixi Qin, Boris Yakobson · 2 October 2026

  Evaluating AI agents on scientific research tasks is constrained by the time and resources required for the underlying experiments or calculations. In computational materials research, repeating the same expensive simulations across agents and trials can make evaluation impractical. We introduce Com…

• MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization

  Shicheng Fang, Yuxin Wang, Zhuo Yang, Xiaohu Xu, Jiahao Lu, Chuanyuan Tan, Tong Zhu, Yining Zheng, Xipeng Qiu · 1 October 2026

  Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relationship to the source molecule. Most instruction-following models instead emit one edited molecule, forcing validity, property improvement, and similarit…

• CARAT: Do Materials LLMs Reason or Recite?

  Jiajun Wu, Jian Yang, Zixiang Ni, Zhenzhu Li, Bin Chong · 1 October 2026

  When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accuracy cannot tell: a structural description often prints the very field it is scored against. CARAT holds question and gold answer fixed across eight …

• ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings

  Ali Shiraee Kasmaee, Mohammad Khodadad, Mahdi Astaraki, Mohammad Arshi Saloot, Nicholas Sherck, Hamidreza Mahyar, Soheila Samiee · 30 September 2026

  Retrieval-Augmented Generation (RAG) systems in chemistry heavily depend on accurate and relevant retrieval of chemical literature. However, general-purpose text embedding models frequently fail to adequately represent complex chemical terminologies, resulting in suboptimal retrieval quality. Existi…

• AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists

  Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun · 30 September 2026

  Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-quality data for training and evaluation. To tackle this data scarcity issue, we present AutoSDT, an automatic pipeline that collects high-quality coding task…

• ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning

  Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song, Xuemin Chen, Tianshu Yu · 30 September 2026

  Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model. One approach is to develop specialized capabilities separately and consolidate them through multi-teacher on-policy distillation, but this raises two questions: how should speci…

• A literature-guided descriptor-based framework for filtering composition search spaces

  Lei Zhang, Markus Stricker · 30 September 2026

  Scientific literature contains latent knowledge about materials behavior, but much of this knowledge is expressed through words, contexts, and recurring associations rather than explicit design principles. This raises a central question: how can large-scale scientific corpora be used for practical p…

• Where Should Physics Enter a Molecular Crystal Generator?

  Haocheng Tang, Junmei Wang, Wengong Jin · 30 September 2026

  Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be introduced during training, post-training, or inference, yet these choices are rarely compared with the generator and physical signal held fixed. We in…

• Learning Transferable Reaction Mechanisms from Visual Chemical Knowledge

  Yujian Yuan, Jiaxin Xu, Xin Cai, Yufan Chen, Zhichao Tan, Ziqi Zhou, Hanyu Gao · 29 September 2026

  Reaction mechanisms describe the step-by-step transformations underlying chemical reactions and are central to reaction analysis and synthesis. Learning-based models have achieved strong performance on established mechanism-prediction benchmarks, but transferring them to unseen chemistry remains cha…

• TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving

  Shengqin Wang, Jie Jin, Yu Cheng, Yihang Chen, Weilin Luo, Yuan Xie, Zhizhong Zhang · 29 September 2026

  Despite the promise of Large Language Models (LLMs) in computational chemistry, rigorous combinatorial chemistry problems remain difficult because they require quantitatively constrained molecular modification, candidate validation, and systematic revision after failed attempts. Existing tool-augmen…

• LLM sequential decision making under uncertainty in biochemical domains

  Mattias Akke, Soojung Yang, Jur\'{g}is Ru\v{z}a, Sathya Edamadaka, Rafael G\'{o}mez-Bombarelli · 29 September 2026

  Large language models (LLMs) are increasingly used to drive scientific discovery. Understanding how LLMs make decisions from new data and memory of the literature is vital before trusting them to design experiments under tight experimental budgets. However, their decision strategies are invisible in…

• CP-Agent: A Harness-Engineered Agent for Crystal Plasticity Simulation Workflows

  Samuel Onimpa Alfred, Abhishek Kumar, Veera Sundararaghavan · 29 September 2026

  Crystal plasticity (CP) simulations predict the mechanical behavior of polycrystalline metals, yet their routine use is hindered by the manual effort of configuring heterogeneous tools, orchestrating multi-step data pipelines, and calibrating constitutive parameters against experiments. These bottle…

• ChemMLLM: Chemical Multimodal Large Language Model

  Qian Tan, Di Zhang, Ben Gao, Peng Xia, Wanhao Liu, Shufei Zhang, Wanli Ouyang, Lei Bai, Yuqiang Li, Tianfan Fu · 28 September 2026

  Recent years have seen rapid progress in multimodal large language models (MLLMs) in the field of chemistry. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, we propose ChemMLLM, a unified chemical multimodal large language mode…

• Scaling Density Functional Theory with Gaussian Splatting

  Andr\'es Guzm\'an-Cordero, Cindy Zhang, Majdi Hassan, Marta Skreta, Kirill Neklyudov, Matija Medvidovi\'c · 28 September 2026

  Density functional theory (DFT) strikes a practical balance between accuracy and computational cost in many problems of computational chemistry and materials science. However, many DFT calculations are limited by fixed atom-centered basis sets, which dictate how accuracy and cost scale with system s…

• AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution

  Tian Luo, Ruge Zhang, Haozhi Han, Yifrng Chen, Yunquan Zhang, Yunxin Liu, Ting Cao, Kun Li · 28 September 2026

  High-fidelity atomistic evolution over long timescales requires more than observing the current crystal configuration. Instantaneous atomistic snapshots are often incomplete: locally similar configurations can correspond to different hidden dynamical contexts, future event preferences, and waiting-t…

• Response-state Learning for Transferable Vibrational Spectroscopic Characterization with Electron Prior

  Zetong Li, Zhuosong Xie, Hengyu Fan, Jiaao Yu, Qiyao Hua, Zheng Lu, Liming Xu, Juanni Wu, Honglin Li · 25 September 2026

  Vibrational spectral prediction can become inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate characteristic spectral fingerprints, making prediction across external chemical space difficult. SO(3) Equivariant Neural Kal…

• The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning

  Kareem M. Gameel, Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr Voznyy · 25 September 2026

  In scientific machine learning, $\Delta$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate that residual scale alone is an insufficient heuristic…

• SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials

  Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan, Henrik H. Kristoffersen, August E. G. Mikkelsen, Xueping Qin, Xin Yang, Heine A. Hansen, Arghya Bhowmik, Tejs Vegge · 24 September 2026

  Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid …

• MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design

  Yongjun Jeong, Hanbum Ko, Ye Rin Kim, Chanhui Lee, Rodrigo Hormazabal, Jaewan Lee, Sehui Han, Sungbin Lim, Sungwoong Kim · 24 September 2026

  Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy multiple constraints, identify infeasible specifications, and reason over multi-step tool outputs. Existing benchmarks do not capture this complexity, f…

• An open benchmark for machine learning-based polymer property prediction

  Robert W. Learsch, Nicholas Liesen, Daniel S. Levine, Anna M. Hiszpanski, Evan R. Antoniuk · 24 September 2026

  Polymer property prediction lacks open, standardized benchmarks that enable rigorous comparison of machine-learning methods, with existing resources covering only a narrow fraction of polymer architectures, such as homopolymers. We introduce Polymer Benchmark 2026 (PolyBench26), an open dataset comp…

• Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction

  L. Ghiringhelli, A. Zambon, G. Tiana · 23 September 2026

  Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample the loss landscape at varying temperatures using Langevin dynamics to characterize the low-loss manifold, and to understand the mechanisms underlying transfor…

• Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence

  Lai Wei, Rongzhi Dong, Ying Feng, Madeline Miklos, Jianjun Hu · 23 September 2026

  Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistently against template-based methods. We evaluate 12 representative generative CSP models, spanning latent-variable, diffusion, flow-matching, autoregres…

• OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery

  Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor, Stefano Martiniani · 23 September 2026

  The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the materials discovery pipeline, but state-of-the-art flow and diffusion models remain bottlenecked by the cost of proposin…

• Topology-Stratified Materials Discovery with A Flow-Based Generative Model

  Jingyi Zhou, Oyshee Chowdhury, Noah Oyeniran, Chongze Hu · 23 September 2026

  Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, …