jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunao Nakanowatari (10024745) | Keisuke Takahashi (1409992) | Hieu Chi Dam (4537027) | Toshiaki Taniike (2343487)
Source: Figshare (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

A Data-ScienceApproach to Experimental Catalyst Discovery:Integrating Exploration, Exploitation, and Serendipity Predicting the performance of heterogeneous catalysts is difficult because it involves complex interactions and unknown elementary reactions; hence, traditional catalyst development relies on trial and error. Machine learning offers a structured approach to address these issues. However, this approach is limited by challenges such as descriptor design, sparse data, and context-dependent interactions. In this study, two machine learning systems were developed to address these challenges in catalyst discovery: a recommender system that balances exploration and exploitation, and a serendipiter that identifies unexpected discoveries for the recommendercatalysts expected to exhibit high performance despite being predicted as most likely non-high-performing. These systems were tested on the oxidative coupling of methane, and the results demonstrated a promising improvement in the efficiency of catalyst discovery. The recommender, based on evidence theory, uses binary combinations of catalyst components as descriptors to predict performance. It handles incomplete data by quantifying contradictions and uncertainty, facilitating a balance between exploration (testing unevidenced catalysts) and exploitation (refining known high-performing ones). The recommender efficiently identified a diverse range of high-performing catalysts through adaptive sampling with 160 catalysts. The serendipiter, a meta-learner, identifies unexpected high-performing catalysts by leveraging different machine learning models. It increased the occurrence of serendipitous discoveries to 50%, compared to 3% with the recommender alone. In summary, these systems improve the efficiency and reproducibility of catalyst discovery by balancing exploitation, exploration, and serendipity. Serendipity ; Recommender system ; Catalysis ; Range (aeronautics) ; Binary number ; Coupling (piping) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Xin Yan (17064) | Haowen Zhong (13766686) | Xiaoxue Wang (311457)
Source: Figshare (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Robust ChemicalReaction Condition Recommendationsvia Label Mix Strategy Recommending optimal reaction conditions remains a core challenge in AI-driven chemistry due to the limited representation of condition features and the sparsity of labeled data. We propose a collaborative filtering framework that encodes reaction conditions using graph neural networks and captures their interaction with reactions through a differential cross-attention mechanism, which selectively focuses on the most relevant condition features. To mitigate noise and sparsity in the data sets, we introduce a Label Mix strategy that augments training signals by blending condition labels, along with co-occurrence matrix regularization to encourage chemically meaningful interactions. Extensive evaluations on multiple benchmark data sets demonstrate the effectiveness of our approach in both full-data and few-shot regimes, with consistent improvements in accuracy, generalization, and robustness. Representation (politics) ; Graph ; Benchmark (surveying) ; Artificial neural network ; Regularization (linguistics) ; Minification ; Noise (video) ; External Data Representation Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Yuta Yahagi | Kiichi Obuchi | F Kosaka | Kota Matsui
Source: ArXiv.org (preprint, 2025-03-20)
Field: Materials Science — Materials Chemistry

Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation 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 transfer learning scheme from first-principles calculations to experiments based on the chemistry-informed domain transformation, that integrates the heterogeneous source and target domains by harnessing the underlying physics and chemistry. The proposed method maps the computational data from the simulation space (source domain) into the space of experimental data (target domain). During this process, these qualitatively different domains are efficiently integrated by a couple of prior knowledge of chemistry, (1) the statistical ensemble, and (2) the relationship between source and target quantities. As a proof-of-concept, we predict the catalyst activity for the reverse water-gas shift reaction by using the abundant first-principles data in addition to the experimental data. Through the demonstration, we confirmed that the transfer learning model exhibits positive transfer in accuracy and data efficiency. In particular, a significantly high accuracy was achieved despite using a few (less than ten) target data in domain transformation, whose accuracy is one order of magnitude smaller than that of a full scratch model trained with over 100 target data. This result indicates that the proposed method leverages the high prediction performance with few target data, which helps to save the number of trials in real laboratories. Transfer of learning ; Domain (mathematical analysis) ; Task (project management) ; Transformation (genetics) ; Experimental data ; Space (punctuation) ; Scratch ; Transfer (computing) Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Advanced Graph Neural Networks
Authors: Soma Kosakai | Hiromasa Kaneko
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Method to Define a Domain in Chemical Space Where Only Stable Molecular Structures Exist Space (punctuation) ; Domain (mathematical analysis) ; Chemical space ; Computer science ; Mathematics ; Chemistry ; Mathematical analysis ; Drug discovery History and advancements in chemistry ; Various Chemistry Research Topics
Authors: Kazimierz Skrobas | Kamila Stefańska‐Skrobas | S. Stelmakh | S. Gierlotka | B. Pałosz
Source: Scientific Reports (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis Three Machine Learning algorithms, namely Random Forest, Neural Networks and Extreme Gradient Boosting, were applied to recognize the shape and surface structure of diamond nanoparticles from powder diffraction data. The algorithms were trained to recognize three types of shapes: 1D - rods, 2D - plates and 3D superspheres and, in the case of plate-like shapes, two types of (111) surfaces, with either one or three dangling bonds per surface atom. The classifiers' training was based on structure functions S(Q) of the nanograin models obtained by Molecular Dynamics simulations. The software tools for models building, diffraction data calculations, and the procedure of grain shape and surface classification are given. It is shown that both Random Forest, Neural Networks and Extreme Gradient Boosting classifiers recognize the shape and surface structure of nanodiamonds with a low number of misclassifications. The derived classifiers were applied to a series of experimental diffraction patterns of diamond nanoparticles with sizes from 1.2 to 3.3 nm. It is shown that ML classification algorithms reproduce very well the results obtained for those samples by real space diffraction data analysis, namely Pair Distribution Function. ML studies prove that the dominating shape of adamantane-synthesized nanodiamonds is a plate terminated by (111) surfaces with 3 dangling bonds of carbon atoms. Extreme learning machine ; Artificial neural network ; Diamond ; Diffraction ; Surface (topology) ; Dangling bond ; Pattern recognition (psychology) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Beng Jit Tan | Chun Feng | Lijuan Zhu | Xiaofeng Bai
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Research Progress in High-Throughput Design of Aluminum Alloys Aluminum alloys are characterized by low density, high strength, good corrosion resistance, thermal conductivity, processability, and cost-effectiveness, making them highly promising for applications in aerospace, mechanical manufacturing, petroleum and chemical engineering, among other fields. Traditional aluminum alloy development relies on trial-and-error methods, which suffer from long cycles and high costs. In contrast, high-throughput technology significantly enhances materials R&amp;D efficiency by integrating parallelized experimentation, computational simulation, and database technologies. This article reviews the evolution of high-throughput experimentation in materials science and introduces common high-throughput fabrication techniques and high-throughput characterization methods. These technologies enable rapid establishment of composition-structure-property relationships, accelerating the screening and optimization of novel aluminum alloys. Furthermore, the integration of machine learning with high-throughput approaches has further advanced the optimization of process parameters, crack prediction, and performance regulation, offering new strategies for aluminum alloy research and development. Finally, the current status and limitations of high-throughput design for aluminum alloys are discussed, along with prospects for their application and future development in the domain of oil and gas drilling and production tubulars. Characterization (materials science) ; Aluminium ; Fabrication ; Alloy ; Corrosion ; Process (computing) ; Domain (mathematical analysis) ; Process design Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Anodic Oxide Films and Nanostructures
Authors: Xin Yan | Haowen Zhong | Xiaoxue Wang
Source: Journal of Chemical Information and Modeling (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Robust Chemical Reaction Condition Recommendations via Label Mix Strategy Recommending optimal reaction conditions remains a core challenge in AI-driven chemistry due to the limited representation of condition features and the sparsity of labeled data. We propose a collaborative filtering framework that encodes reaction conditions using graph neural networks and captures their interaction with reactions through a differential cross-attention mechanism, which selectively focuses on the most relevant condition features. To mitigate noise and sparsity in the data sets, we introduce a Label Mix strategy that augments training signals by blending condition labels, along with co-occurrence matrix regularization to encourage chemically meaningful interactions. Extensive evaluations on multiple benchmark data sets demonstrate the effectiveness of our approach in both full-data and few-shot regimes, with consistent improvements in accuracy, generalization, and robustness. Benchmark (surveying) ; Representation (politics) ; Graph ; Regularization (linguistics) ; Artificial neural network ; Deep neural networks ; Noise (video) ; Reaction conditions Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Dong-Hee Shin | Young-Han Son | Tae‐Eui Kam
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Sharpness-Aware Minimization with Physics-Informed Regularizations for Predicting Semiconductor Material Properties in Molecular Dynamics Dynamics (music) ; Minification ; Molecular dynamics ; Statistical physics ; Semiconductor ; Physics ; Computer science ; Computational physics ; Mathematics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Hongyue Wang | Jia-Lan Chen | Xin‐Rui Qi | Xuechun Jiang | Junyi Yang | Jin Li | Chi Ruan | Wei‐Xue Li | Jin‐Xun Liu
Source: Journal of the American Chemical Society (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Operando Cluster Catalysis via Coupled Surface–Subsurface Dynamics Catalytic surfaces and subsurfaces undergo continuous restructuring under reaction conditions, yet how coupled surface-subsurface dynamics governs the emergence and performance of active sites remains unresolved. Here, we introduce a machine-learning-accelerated multiscale framework that integrates grand-canonical Monte Carlo sampling, neural-network molecular dynamics, and first-principles microkinetics to resolve operando catalyst restructuring at the atomic scale. Using Pd-catalyzed acetylene hydrogenation as a prototypical system, we show that adsorbed hydrocarbons weaken Pd-Pd bonds, whereas subsurface carbon anchors low-coordination atoms, together promoting the operando formation of Pd<sub>1</sub> single atoms and Pd<sub>2</sub>, Pd<sub>3</sub>, Pd<sub>6</sub>, and Pd<sub>10</sub> clusters. A population-weighted activity analysis identifies Pd<sub>10</sub> as the dominant active ensemble, achieving an ∼36,000-fold rate enhancement and >99% ethylene selectivity over clean and hydrocarbon-covered Pd surfaces. A structure-activity landscape based on cluster height further quantifies this relationship. Extending this approach to Ag, Cu, Au, Ni, Rh, and Pt reveals that operando cluster formation requires moderate hydrocarbon coadsorption and subsurface carbon. This transferable approach reveals how coupled surface-subsurface dynamics govern the emergence and performance of active sites, offering broad applicability to other reactions in complex environments. Acetylene ; Catalysis ; Cluster (spacecraft) ; Hydrocarbon ; Molecular dynamics ; Selectivity ; Ethylene ; Adsorption Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Ying Xu | Ziyu Li | Yuting Xiao | Yu-Zhe Hu | Qi Yang | Xiao‐Nan Wu | Sheng‐Gui He
Source: ChemPhysChem (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Machine Learning Study of Methane Activation by O‐Centered Radicals over Metal Oxide Clusters Methane activation, a "holy grail" in chemistry, is crucial for producing value-added chemicals. Metal oxide clusters (MOCs) that activate methane through oxygen-centered radicals (O<sup>•-</sup>) have been extensively studied. However, a systematic and quantitative understanding of the electronic factors that govern the reactivity of the O<sup>•-</sup> radicals toward methane is still missing. Herein, a machine learning model has been developed to quantitatively describe the reactivity of MOCs toward CH<sub>4</sub> by incorporating 17 newly obtained experimental reaction rate constants alongside data accumulated from the literature, a total of 107 in number, as well as descriptors derived from density functional theory calculations. Utilizing the back propagation artificial neural network algorithm, the model described with only two key features-unpaired spin density (UPSD) and local charge (Q<sub>L</sub>)-is capable of predicting CH<sub>4</sub> activation reactivity of O<sup>•-</sup> containing MOCs across a wide range of metal elements and cluster compositions. Further investigations indicate that a feature related to the detachment or attachment of electrons can replace Q<sub>L</sub> while UPSD is irreplaceable. By using artificial intelligence, this study has made a big step forward in understanding methane activation by reactive oxygen species. Radical ; Methane ; Oxide ; Metal ; Chemistry ; Photochemistry ; Inorganic chemistry ; Materials science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Joseph J. Gair | Corin Wagen | Rebecca Brew | I. Nelson | Meruyert Binayeva | Amlan Nayak | W. Kyle Simmons
Source: ChemRxiv (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Wiggle150: Benchmarking Density Functionals And Neural Network Potentials On Highly Strained Conformers Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving non-equilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometries—generated via metadynamics and scored using DLPNO-CCSD(T)/CBS reference energies—exhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed–accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving non-equilibrium systems and guide the development of new density functionals and neural network potentials. Benchmarking ; Conformational isomerism ; Artificial neural network ; Materials science ; Computational chemistry ; Statistical physics ; Physics ; Computer science ; Business ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science
Authors: P. Mitra | Hrishit Banerjee
Source: ChemRxiv (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Deep Learning-based prediction of self-energies from ab initio Dynamical Mean-Field Theory for real materials with minimal data sets Ab initio ; Set (abstract data type) ; Quantum ; Training set ; Electronic structure ; Computation ; Experimental data ; Ab initio quantum chemistry methods Machine Learning in Materials Science
Authors: Hao Wang (39217) | Yuquan Li (2098042) | Xiaoyang Xuan (20961526) | Kai Wang (21246) | Ye-feng Yao (9249351) | Likun Pan (1411930)
Source: Figshare (article, 2025-03-30)
Field: Materials Science — Materials Chemistry

Machine LearningAccelerated Discovery of CovalentOrganic Frameworks for Environmental and Energy Applications Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO<sub>2</sub> capture, CH<sub>4</sub> storage, gas separation, catalysis, etc. Traditional methods of material research, which mainly rely on manual experimentation, are not particularly efficient, while with advancements in computer science, high-throughput computational screening methods based on molecular simulation have become crucial in material discovery, yet they face limitations in terms of computational resources and time. Currently, machine learning (ML) has emerged as a transformative tool in many fields, capable of analyzing large data sets, identifying underlying patterns, and predicting material performance efficiently and accurately. This approach, termed “materials genomics”, combines high-throughput computational screening with ML to predict and design high-performance materials, significantly speeding up the discovery process compared to traditional methods. This review discusses the functions of ML in the screening, design, and performance prediction of COFs and highlights their applications across various domains like CO<sub>2</sub> capture, CH<sub>4</sub> storage, gas separation, and catalysis, thereby providing new research directions and enhancing the understanding of COF materials and their applications. Process (computing) ; Transformative learning ; Computational model ; Energy (signal processing) ; Deep learning ; Range (aeronautics) ; Porous medium Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Surface Chemistry and Catalysis
Authors: Hong-Yue Wang (22566608) | Jia-Lan Chen (17992818) | Xin-Ze Qi (22398468) | Xue-Chun Jiang (22398471) | Jun-Yi Yang (112196) | Jin Li (119007) | Chuan-Liang Ruan (22566611) | Wei-Xue Li (1555876) | Jin-Xun Liu (1672297)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

OperandoCluster Catalysis via Coupled Surface–SubsurfaceDynamics Catalytic surfaces and subsurfaces undergo continuous restructuring under reaction conditions, yet how coupled surface–subsurface dynamics governs the emergence and performance of active sites remains unresolved. Here, we introduce a machine-learning-accelerated multiscale framework that integrates grand-canonical Monte Carlo sampling, neural-network molecular dynamics, and first-principles microkinetics to resolve operando catalyst restructuring at the atomic scale. Using Pd-catalyzed acetylene hydrogenation as a prototypical system, we show that adsorbed hydrocarbons weaken Pd–Pd bonds, whereas subsurface carbon anchors low-coordination atoms, together promoting the operando formation of Pd<sub>1</sub> single atoms and Pd<sub>2</sub>, Pd<sub>3</sub>, Pd<sub>6</sub>, and Pd<sub>10</sub> clusters. A population-weighted activity analysis identifies Pd<sub>10</sub> as the dominant active ensemble, achieving an ∼36,000-fold rate enhancement and >99% ethylene selectivity over clean and hydrocarbon-covered Pd surfaces. A structure–activity landscape based on cluster height further quantifies this relationship. Extending this approach to Ag, Cu, Au, Ni, Rh, and Pt reveals that operando cluster formation requires moderate hydrocarbon coadsorption and subsurface carbon. This transferable approach reveals how coupled surface–subsurface dynamics govern the emergence and performance of active sites, offering broad applicability to other reactions in complex environments. Acetylene ; Catalysis ; Cluster (spacecraft) ; Molecular dynamics ; Hydrocarbon ; Adsorption ; Selectivity ; Ethylene Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jiantao Wang | Yichi Zhang | Yan Liu | Huiling Deng | Mingfeng Liu | Yan Sun | Bing Li | Xing‐Qiu Chen | Peitao Liu
Source: Journal of Material Science and Technology (preprint, 2025-11-19)
Field: Materials Science — Materials Chemistry

Atomistic mechanisms of phase transitions in all-temperature barocaloric material KPF6 Monoclinic crystal system ; Phase transition ; Molecular dynamics ; Anharmonicity ; Isothermal process ; Plastic crystal ; Phase (matter) ; Lattice (music) ; Octahedron Thermal Expansion and Ionic Conductivity ; Ferroelectric and Piezoelectric Materials ; Inorganic Chemistry and Materials