authors stringlengths 11 2.41k | title stringlengths 38 184 | journal-ref stringclasses 115
values | doi stringlengths 17 34 ⌀ | report-no stringclasses 3
values | categories stringlengths 17 83 | abstract stringlengths 124 1.92k | versions stringlengths 62 689 | update_date stringdate 2007-09-13 00:00:00 2025-05-15 00:00:00 |
|---|---|---|---|---|---|---|---|---|
Franz Damma{\ss}, Karl A. Kalina, Markus K\"astner | When invariants matter: The role of I1 and I2 in neural network models
of incompressible hyperelasticity | null | null | null | cond-mat.mtrl-sci cond-mat.soft | For the formulation of machine learning-based material models, the usage of
invariants of deformation tensors is attractive, since this can a priori
guarantee objectivity and material symmetry. In this work, we consider
incompressible, isotropic hyperelasticity, where two invariants I1 and I2 are
required for depicti... | [{'version': 'v1', 'created': 'Wed, 26 Mar 2025 14:43:11 GMT'}] | 2025-03-27 |
Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra, Kohei
Shinohara, Chikashi Shinagawa, So Takamoto, Ju Li | Efficient Crystal Structure Prediction Using Genetic Algorithm and
Universal Neural Network Potential | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Crystal structure prediction (CSP) is crucial for identifying stable crystal
structures in given systems and is a prerequisite for computational atomistic
simulations. Recent advances in neural network potentials (NNPs) have reduced
the computational cost of CSP. However, searching for stable crystal structures
acros... | [{'version': 'v1', 'created': 'Thu, 27 Mar 2025 06:38:59 GMT'}] | 2025-03-28 |
Somayeh Hosseinhashemi, Philipp Rieder, Orkun Furat, Benedikt
Prifling, Changlin Wu, Christoph Thon, Volker Schmidt, Carsten Schilde | Statistical learning of structure-property relationships for transport
in porous media, using hybrid AI modeling | null | null | null | cond-mat.mtrl-sci cs.LG | The 3D microstructure of porous media, such as electrodes in lithium-ion
batteries or fiber-based materials, significantly impacts the resulting
macroscopic properties, including effective diffusivity or permeability.
Consequently, quantitative structure-property relationships, which link
structural descriptors of 3D... | [{'version': 'v1', 'created': 'Thu, 27 Mar 2025 14:46:40 GMT'}] | 2025-03-31 |
Izumi Takahara, Teruyasu Mizoguchi and Bang Liu | Accelerated Inorganic Materials Design with Generative AI Agents | null | null | null | cond-mat.mtrl-sci | Designing inorganic crystalline materials with tailored properties is
critical to technological innovation, yet current generative computational
methods often struggle to efficiently explore desired targets with sufficient
interpretability. Here, we present MatAgent, a generative approach for
inorganic materials disc... | [{'version': 'v1', 'created': 'Tue, 1 Apr 2025 12:51:07 GMT'}] | 2025-04-02 |
Zhendong Cao, Lei Wang | CrystalFormer-RL: Reinforcement Fine-Tuning for Materials Design | null | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph | Reinforcement fine-tuning has instrumental enhanced the instruction-following
and reasoning abilities of large language models. In this work, we explore the
applications of reinforcement fine-tuning to the autoregressive
transformer-based materials generative model CrystalFormer (arXiv:2403.15734)
using discriminativ... | [{'version': 'v1', 'created': 'Thu, 3 Apr 2025 07:59:30 GMT'}] | 2025-04-04 |
Yuta Yahagi, Kiichi Obuchi, Fumihiko Kosaka, Kota Matsui | Transfer learning from first-principles calculations to experiments with
chemistry-informed domain transformation | null | null | null | physics.chem-ph cond-mat.mtrl-sci cs.LG physics.comp-ph | Simulation-to-Real (Sim2Real) transfer learning, the machine learning
technique that efficiently solves a real-world task by leveraging knowledge
from computational data, has received increasing attention in materials science
as a promising solution to the scarcity of experimental data. We proposed an
efficient trans... | [{'version': 'v1', 'created': 'Thu, 20 Mar 2025 15:14:23 GMT'}, {'version': 'v2', 'created': 'Mon, 7 Apr 2025 07:29:31 GMT'}] | 2025-04-08 |
Lingyu Kong, Nima Shoghi, Guoxiang Hu, Pan Li, Victor Fung | MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning
Atomistic Foundation Models to Accelerate Materials Simulation and Discovery | null | null | null | cond-mat.mtrl-sci cs.AI cs.LG | Geometric machine learning models such as graph neural networks have achieved
remarkable success in recent years in chemical and materials science research
for applications such as high-throughput virtual screening and atomistic
simulations. The success of these models can be attributed to their ability to
effectivel... | [{'version': 'v1', 'created': 'Mon, 14 Apr 2025 19:12:43 GMT'}] | 2025-04-16 |
Yahao Dai, Henry Chan, Aikaterini Vriza, Fredrick Kim, Yunfei Wang,
Wei Liu, Naisong Shan, Jing Xu, Max Weires, Yukun Wu, Zhiqiang Cao, C.
Suzanne Miller, Ralu Divan, Xiaodan Gu, Chenhui Zhu, Sihong Wang, Jie Xu | Adaptive AI decision interface for autonomous electronic material
discovery | null | null | null | cond-mat.mtrl-sci cs.AI | AI-powered autonomous experimentation (AI/AE) can accelerate materials
discovery but its effectiveness for electronic materials is hindered by data
scarcity from lengthy and complex design-fabricate-test-analyze cycles. Unlike
experienced human scientists, even advanced AI algorithms in AI/AE lack the
adaptability to... | [{'version': 'v1', 'created': 'Thu, 17 Apr 2025 21:26:48 GMT'}] | 2025-04-21 |
Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan, Yen-hsu Lin, Jian
Yin, Saber Mirzaei, Mona Abdelgaid, Ali H. Alawadhi, KwangHwan Cho, Zhiling
Zheng, Ekin Dogus Cubuk, Christian Borgs, Jennifer T. Chayes, Kristin A.
Persson, Omar M. Yaghi | System of Agentic AI for the Discovery of Metal-Organic Frameworks | null | null | null | cond-mat.mtrl-sci cs.AI cs.CL cs.MA | Generative models and machine learning promise accelerated material discovery
in MOFs for CO2 capture and water harvesting but face significant challenges
navigating vast chemical spaces while ensuring synthetizability. Here, we
present MOFGen, a system of Agentic AI comprising interconnected agents: a
large language... | [{'version': 'v1', 'created': 'Fri, 18 Apr 2025 23:54:25 GMT'}] | 2025-04-22 |
Ahmed Sobhi Saleh, Kristof Croes, Hajdin Ceric, Ingrid De Wolf, Houman
Zahedmanesh | Novel Concept-Oriented Synthetic Data approach for Training Generative
AI-Driven Crystal Grain Analysis Using Diffusion Model | Computational Materials Science, Vol 251 (2025) | 10.1016/j.commatsci.2025.113723 | null | cs.LG cond-mat.mtrl-sci | The traditional techniques for extracting polycrystalline grain structures
from microscopy images, such as transmission electron microscopy (TEM) and
scanning electron microscopy (SEM), are labour-intensive, subjective, and
time-consuming, limiting their scalability for high-throughput analysis. In
this study, we pre... | [{'version': 'v1', 'created': 'Mon, 21 Apr 2025 00:46:28 GMT'}] | 2025-04-22 |
Clement Vincely, Amin Reiners-Sakic, Vedant Dave, Erwin
Povoden-Karadeniz, Elmar Rueckert, Ronald Schnitzer, David Holec | Amending CALPHAD databases using a neural network for predicting mixing
enthalpy of liquids | null | null | null | physics.chem-ph cond-mat.dis-nn cond-mat.mtrl-sci | In order to establish the thermodynamic stability of a system, knowledge of
its Gibbs free energy is essential. Most often, the Gibbs free energy is
predicted within the CALPHAD framework using models employing thermodynamic
properties, such as the mixing enthalpy, heat capacity, and activity
coefficients. Here, we p... | [{'version': 'v1', 'created': 'Fri, 25 Apr 2025 14:08:28 GMT'}, {'version': 'v2', 'created': 'Mon, 28 Apr 2025 05:44:02 GMT'}] | 2025-04-29 |
Zuhong Lin, Daoyuan Ren, Kai Ran, Sun Jing, Xiaotiang Huang, Haiyang
He, Pengxu Pan, Xiaohang Zhang, Ying Fang, Tianying Wang, Minli Wu, Zhanglin
Li, Xiaochuan Zhang, Haipu Li, Jingjing Yao | Reshaping MOFs Text Mining with a Dynamic Multi-Agent Framework of Large
Language Agents | null | null | null | cs.AI cond-mat.mtrl-sci | The mining of synthesis conditions for metal-organic frameworks (MOFs) is a
significant focus in materials science. However, identifying the precise
synthesis conditions for specific MOFs within the vast array of possibilities
presents a considerable challenge. Large Language Models (LLMs) offer a
promising solution ... | [{'version': 'v1', 'created': 'Sat, 26 Apr 2025 09:55:04 GMT'}] | 2025-04-29 |
Yomn Alkabakibi, Congwei Xie, Artem R. Oganov | Graph Neural Network Prediction of Nonlinear Optical Properties | null | null | null | cond-mat.mtrl-sci cs.LG physics.optics | Nonlinear optical (NLO) materials for generating lasers via second harmonic
generation (SHG) are highly sought in today's technology. However, discovering
novel materials with considerable SHG is challenging due to the time-consuming
and costly nature of both experimental methods and first-principles
calculations. In... | [{'version': 'v1', 'created': 'Mon, 28 Apr 2025 17:03:22 GMT'}] | 2025-04-29 |
Ivan Pinto-Huguet, Marc Botifoll, Xuli Chen, Martin Borstad Eriksen,
Jing Yu, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Jordi Arbiol | Enhancing atomic-resolution in electron microscopy: A frequency-domain
deep learning denoiser | null | null | null | cond-mat.mtrl-sci | Atomic resolution electron microscopy, particularly high-angle annular
dark-field scanning transmission electron microscopy, has become an essential
tool for many scientific fields, when direct visualization of atomic
arrangements and defects are needed, as they dictate the material's functional
and mechanical behavi... | [{'version': 'v1', 'created': 'Sat, 3 May 2025 11:31:53 GMT'}] | 2025-05-06 |
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali, Mehrad Ansari, L.
Catherine Brinson, Yuan Chiang, Defne Circi, Min-Hsueh Chiu, Nathan Daelman,
Matthew L. Evans, Abhijeet S. Gangan, Janine George, Hassan Harb, Ghazal
Khalighinejad, Sartaaj Takrim Khan, Sascha Klawohn, Magdalena Lederbauer,
Soroush Mahjoubi, ... | 34 Examples of LLM Applications in Materials Science and Chemistry:
Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery | null | null | null | cs.LG cond-mat.mtrl-sci | Large Language Models (LLMs) are reshaping many aspects of materials science
and chemistry research, enabling advances in molecular property prediction,
materials design, scientific automation, knowledge extraction, and more. Recent
developments demonstrate that the latest class of models are able to integrate
struct... | [{'version': 'v1', 'created': 'Mon, 5 May 2025 22:08:37 GMT'}] | 2025-05-07 |
Ting-Wei Hsu, Zhenyao Fang, Arun Bansil, and Qimin Yan | Accurate Prediction of Sequential Tensor Properties Using Equivariant
Graph Neural Network | null | null | null | cond-mat.mtrl-sci physics.optics | Optical spectra serve as a powerful tool for probing the interactions between
materials and light, unveiling complex electronic structures such as flat bands
and nontrivial topological features. These insights are crucial for the
development and optimization of photonic devices, including solar cells,
light-emitting ... | [{'version': 'v1', 'created': 'Thu, 8 May 2025 00:10:41 GMT'}] | 2025-05-09 |
Pungponhavoan Tep and Marc Bernacki | High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast
Computations | null | null | null | cond-mat.mtrl-sci cs.AI | Grain growth simulation is crucial for predicting metallic material
microstructure evolution during annealing and resulting final mechanical
properties, but traditional partial differential equation-based methods are
computationally expensive, creating bottlenecks in materials design and
manufacturing. In this work, ... | [{'version': 'v1', 'created': 'Thu, 8 May 2025 15:43:40 GMT'}] | 2025-05-09 |
Jamie Holber and Krishna Garikipati | Equivariant graph neural network surrogates for predicting the
properties of relaxed atomic configurations | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Density functional theory (DFT) calculations determine the relaxed atomic
positions and lattice parameters that minimize the formation energy of a
structure. We present an equivariant graph neural network (EGNN) model to
predict the outcome of DFT calculations for structures of interest. Cluster
expansions are a well... | [{'version': 'v1', 'created': 'Mon, 12 May 2025 23:22:35 GMT'}] | 2025-05-14 |
Purun-hanul Kim, Jeong Min Choi, Seungwu Han, Youngho Kang | Neural Network-Driven Molecular Insights into Alkaline Wet Etching of
GaN: Toward Atomistic Precision in Nanostructure Fabrication | null | null | null | cond-mat.mtrl-sci | We present large-scale molecular dynamics (MD) simulations based on a
machine-learning interatomic potential to investigate the wet etching behavior
of various GaN facets in alkaline solution-a process critical to the
fabrication of nitride-based semiconductor devices. A Behler-Parrinello-type
neural network potentia... | [{'version': 'v1', 'created': 'Tue, 13 May 2025 02:01:07 GMT'}] | 2025-05-14 |
Xinyu You, Xiang Liu, Chuan-Shen Hu, Kelin Xia and Tze Chien Sum | Quotient Complex Transformer (QCformer) for Perovskite Data Analysis | null | null | null | cs.LG cond-mat.mtrl-sci | The discovery of novel functional materials is crucial in addressing the
challenges of sustainable energy generation and climate change. Hybrid
organic-inorganic perovskites (HOIPs) have gained attention for their
exceptional optoelectronic properties in photovoltaics. Recently, geometric
deep learning, particularly ... | [{'version': 'v1', 'created': 'Wed, 14 May 2025 06:13:14 GMT'}] | 2025-05-15 |
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