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AI for Science
ISSN 3050-287X · IOP Publishing · 38 artikel di indeks
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Artikel Jurnal
PhononBench:a large-scale phonon-based benchmark for dynamical stability in crystal generation
Generative models based on graph neural networks, diffusion models, and large language models have advanced the design of crystalline materials. Existing evaluations commonly follow the stability–uniqueness–novelty (S.U.N.) framework, where stability is primarily assessed thermodynamically and does…
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Artikel Jurnal
AtomFlow: generative prediction of chemical reactions in 3D space
Accurately predicting the outcomes of chemical reactions is of great significance for many applications ranging from drug discovery to catalyst design, yet the development of generative machine-learning models for materials science and chemical processes remains at an early stage. Current…
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Artikel Jurnal
AI-guided design of optoelectronic molecularly modified CH3NH3PbI3 perovskite thin films with improved aqueous stability
The inherent vulnerability of halide perovskite films to moisture and water exposure severely restricts their stability, posing a challenge for industrial implementation. In this study, photoelectrochemical experiments, machine learning, and first-principles calculations are employed to accelerate…
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Artikel Jurnal
Bioinspired123D: generative 3D modeling system for bioinspired structures
Generative AI has made rapid progress in text, image, and video synthesis, yet text-to-3D modeling for scientific design remains particularly challenging due to limited controllability and high computational cost. Most existing 3D generative methods rely on meshes, voxels, or point clouds which can…
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Artikel Jurnal
D3REAM: a framework for full-space inverse materials design with target properties via machine learning models and global optimization algorithms
Full-space inverse materials design (FSIMD) aims to identify materials with target properties by exploring the complete combinatorial space of possible elements, compositions, and structures, yet efficiently navigating the vast and high-dimensional materials space remains a fundamental challenge…
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Artikel Jurnal
Data-driven graph neural networks guide codoping for stable Ni-rich layered cathodes
Ni-rich layered oxide cathode materials have emerged as promising candidates for next-generation mainstream high-energy nonaqueous lithium-based batteries because of their inherent advantages in terms of specific capacity. However, the delicate layered structure is more susceptible to both crystal…
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Artikel Jurnal
THEMol dataset: torsion, Hessian, and energy of molecules
We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to 50 heavy atoms. THEMol includes a Hessian subset with more than 3 million relaxed geometries with Hessian matrices, a…
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Artikel Jurnal
Intelligent design of metamaterials for magnetic resonance imaging: a review
Electromagnetic (EM) metamaterials (MMs) enable precise control of the magnetic resonance imaging (MRI) radiofrequency (RF) near-field via subwavelength architectures, thereby improving the receive RF-field ( $B_1^{ - {\text{ }}}$ ) distribution and constraining the specific absorption rate…
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Artikel Jurnal
Machine-learned structural order and the Lindemann criterion
The Lindemann criterion relates crystalline instability to atomic displacement, but as a scalar criterion it does not distinguish the loss of a specific lattice identity from the loss of generic solid-like order. We test this distinction by mapping atomistic configurations onto voxelized…
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Perspectives on thermodynamics for AI and AI safety
Artificial intelligence (AI) and human intelligence share deep thermodynamic foundations: both operate as systems that convert environmental energy and entropy into ordered, predictive structures under the constraints imposed by boundary conditions. Zentropy theory, an integration of quantum…
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Artikel Jurnal
From prediction to discovery: AI as an observatory of physical organization in protein space
Artificial intelligence (AI) has transformed protein science from a data-sparse field into one increasingly rich in model-derived information. Predicted structures, sequence embeddings, confidence measures, mutation scores, inverse-design outputs, and generative ensembles provide complementary…
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Artikel Jurnal
QCell: comprehensive quantum-mechanical dataset spanning diverse biomolecular fragments
Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computational efficiency of mechanistic potentials. However, the development of reliable MLFFs for biomolecular systems remains…
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MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning
The vast and multiscale nature of chemical knowledge—from molecular structures to material properties—presents significant challenges for both human researchers and artificial intelligence (AI) systems. While large language models (LLMs) can process chemical information, they operate as black boxes…
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Artikel Jurnal
Jigsaw-like knowledge graph generation: a study on generalization patterns with a LightRAG implementation
The integration of knowledge graphs (KGs) with retrieval-augmented generation (RAG) has significantly advanced domain-specific question-answering systems. However, a critical limitation persists in existing KG-based RAG frameworks: the inability to efficiently handle localized updates within a…
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Artikel Jurnal
Revealing fast ionic conduction in solid electrolytes through machine learning accelerated Raman calculations
Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the…
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Artikel Jurnal
Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop
As advanced amorphous materials with superior mechanical, physical, and chemical properties, metallic glasses (MGs) hold significant promise for a wide range of applications. However, their rational design and precise property control have long been impeded by notable challenges. These obstacles…
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A Pretrain-Finetune-Distill framework for machine learning force fields in doping engineering of solid-state electrolytes
Doping engineering is a key strategy to improve ionic conductivity of solid ionic conductors, but the vast chemical composition space poses significant challenges for experimental optimization. Computational methods, such as ab initio or classical molecular dynamics, suffer from a critical…
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Artikel Jurnal
Learning atomic representations for data-driven materials design
Learning the latent representations of atomic structures has become central to the application of machine learning (ML) in materials science, as such representations provide a unified framework for connecting atomic structures to material properties. Early physics-inspired descriptors facilitated…
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Artikel Jurnal
Beyond Adam: disentangling optimizer effects in the fine-tuning of atomistic foundation models
Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability across chemical spaces. Although fine-tuning is essential for adapting these pretrained models to specific target…
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Artikel Jurnal
Artificial intelligence driven workflow for accelerating design of novel photosensitizers
The discovery of high-performance photosensitizers has long been hindered by the time-consuming and resource-intensive nature of traditional trial-and-error approaches. Here, we present A I- A ccelerated P hoto S ensitizer I nnovation (AAPSI), a comprehensive workflow that integrates expert…