jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akash Ball | Gianmarco Terrones | Shuwen Yue | Heather J. Kulik
Source: ChemRxiv (preprint, 2025-03-19)
Field: Chemistry — Inorganic Chemistry

Data-driven discovery of water-stable metal-organicframeworks with high water uptake capacity Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination and atmospheric water harvesting, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identifying water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with twelve diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to be both water-stable and have high water uptake. Metal-organic framework ; Environmental science ; Chemistry ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Ibrahim Shomope | Amani Al-Othman | Muhammad Tawalbeh | Hussam Alshraideh
Source: Energy (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction and optimization of solid oxide electrolysis cells for green hydrogen production using RF, FFNN, and PSO approaches Hydrogen production ; Electrolyte ; Particle swarm optimization ; Hydrogen ; Electrolysis ; Response surface methodology ; Polymer electrolyte membrane electrolysis ; Volume (thermodynamics) ; Cathode Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Hybrid Renewable Energy Systems
Authors: Hong-an Lu | Yongxin Cui | Junhao Ma | Jieru Sun | Qian Zou | Yi Wu | Huailiang Fu | Chonggui Zhong | Lei Zhang | Pengxia Zhou
Source: Materials Today Communications (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Employing interpretable multi-output machine learning to predict stable perovskites in photovoltaics Materials science ; Photovoltaics ; Machine learning ; Artificial intelligence ; Photovoltaic system ; Nanotechnology ; Engineering physics ; Computer science ; Electrical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Z.J. Deng | Kailing Fang | Chong Guo | Zhichao Gong | Haojie Yue | Huacheng Zhang | Kang Li | Kun Guo | Zhiyong Liu | Bing Xie | Jinshan Lu | Kui Yao | Francis E. H. Tay
Source: Materials (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Prediction of ABX3 Perovskite Formation Energy Using Machine Learning Materials with perovskite phases are widely used in solar cells and ferroelectric, piezoelectric, dielectric and superconducting devices due to their various notable functions. However, structural instability limits some compositions in forming robust perovskite phases for device applications. The analytical approach using the tolerance factor (t) can only guarantee prediction accuracy within a limited range, ascribed to its nature of overlooking the atomic interaction. Hence, here we establish a prediction model using formation energy as the target parameter for its reflection of the reaction of atoms and apply machine learning as the analysis method since it has been successfully employed in plenty of material property prediction studies. Machine learning employs statistical methodologies to identify correlative patterns within large-scale datasets, enabling accurate predictions with robust generalization. In this work, we built a model to predict the formation energy of ABX<sub>3</sub> perovskite using machine learning and achieved a model with an R-squared value of 0.928 and a root mean square error of 0.301 eV/atom, validated by first-principles computations. In total, 75% of the values were correctly predicted within an error lower than 0.06. This work could contribute to accelerating the study of solving perovskites' instability. Perovskite (structure) ; Materials science ; Computer science ; Artificial intelligence ; Chemical engineering ; Engineering Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides
Authors: Arthur Lin | Lucas Ortengren | Seok-Hyun Hwang | Yong-Cheol Cho | Jigyasa Nigam | Rose K. Cersonsky
Source: The Journal of Open Source Software (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems AniSOAP is a package that translates coarse-grained molecular configurations into tensorial representations that are ideal for supervised machine-learning models of thermodynamic quantities and unsupervised data-driven analyses.It generalizes several popular spherical representations for atomistic ML and aims to bridge the gap between two scientific communities: the machine-learning enabled atomistic simulation community, which leverages ML to accelerate and refine quantum modeling of complex interactions between spherical atoms, and the coarsegrained and colloid modeling community, which focuses on understanding emergent behavior of macroscopic particles with (plausibly) complex geometries.AniSOAP provides a common framework to tackle scientific questions at the intersection of these two fields. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Xin Jin | Yutao Li | Kelian Gaedecke | Xiaoqi Zhang | Berend Smit
Source: ChemRxiv (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120 Metal-organic frameworks (MOFs) are porous materials with the potential for gas adsorption and separation technologies due to their tunable structural and chemical characteristics. However, simulating gas adsorption isotherms in MOFs with DFT-level accuracy remains a key challenge. In this work, we take MIL-120 as a case study and propose a complete computational workflow to fine-tune a pre-trained MACE potential, enabling accurate machine-learning interatomic potentials tailored for frameworks with dynamic structural behavior. Using this ML potential to accelerate sampling, we uncover a strong coupling between CO2 adsorption and the complex dynamic hydrogen-bond network on the MIL-120 pore surface. The presence of CO2 induces local configurational rearrangements that reshape the pore environment. This insight provides a new perspective for the structural design of MOFs for direct air capture (DAC), and offers a generalizable strategy for simulating adsorption in other flexible MOF systems. Hydrogen bond ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science
Authors: Qingyuan Wang | X. C. Yang | Ruo-Bing Shi | Yiming Ren | Xuesong Fan | Zhen Qin | Jun Chen
Source: Applied Physics A (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Discovery of novel High-Tc superconductors via machine learning-based random forest model Superconductivity ; Random forest ; Materials science ; Condensed matter physics ; Computer science ; Statistical physics ; Chemistry ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Superconducting Materials and Applications ; Nuclear Materials and Properties
Authors: Jinhua Yu | Xinxin Zhao | Kaihui Xun | S. Zhang | Lu Jiang | Jun Ding
Source: Carbon (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Machine learning-driven atomistic simulation of mechanical deformation in nanoporous amorphous carbon Nanoporous ; Materials science ; Deformation (meteorology) ; Amorphous solid ; Carbon fibers ; Composite material ; Amorphous carbon ; Molecular dynamics ; Nanotechnology ; Crystallography ; Chemistry ; Computational chemistry ; Composite number Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Bin Liu | Zhiguo Tian | Alexander A. Barinov | Moran Wang
Source: International Journal of Heat and Mass Transfer (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Interfacial and coherent thermal transport of phonons in Bi <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si305.svg" display="inline" id="d1e697"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> Te <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si306.svg" display="inline" id="d1e705"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> /Sb <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si305.svg" display="inline" id="d1e713"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> Te <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si306.svg" display="inline" id="d1e722"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> superlattices Phonon ; Thermal conductivity ; Thermoelectric effect ; Superlattice ; Thermoelectric materials ; Phonon scattering ; Scattering ; Lattice (music) Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermography and Photoacoustic Techniques
Authors: Yongxue Zhu | Wenhui Ma | Xingzai Chai | Yunpeng Gu | Yuanxin Wu | Haisheng Chen | Ting Zhang
Source: Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advances in Catalysts for Magnesium-Based Hydrogen Storage Materials MgH<sub>2</sub> (magnesium hydride) has attracted attention as a potential hydrogen storage material owing to its rich availability and high theoretical hydrogen capacity. Nevertheless, its practical utilization is restricted by its high thermodynamic stability and slow hydrogen sorption kinetics. Recent advancements have demonstrated that incorporating various catalytic systems-such as transition metals, metal oxides, metal halides, metal sulfides, and carbon-supported materials-effectively improves hydrogen dissociation, diffusion, and Mg-H bond modulation. Structural transformations, interfacial interactions, and synergistic effects in multicomponent systems substantially enhance MgH<sub>2</sub> performance. Furthermore, cutting-edge computational techniques such as DFT (density functional theory) and ML (machine learning) have become indispensable for expediting catalyst development and forecasting their performance. These computational techniques enable high-throughput screening, provide atomic-scale insights into catalytic mechanisms, and substantially reduce experimental workloads. This review systematically summarizes recent progress in catalytic modifications of MgH<sub>2</sub>, elucidates the underlying enhancement mechanisms, highlights the contributions of DFT and ML methodologies, and discusses future directions such as nanostructuring, multifunctional composite catalysts, and computational-driven rational catalyst design for next-generation hydrogen storage technologies. Catalysis ; Hydrogen storage ; Rational design ; Expediting ; Hydrogen ; Hydrogen economy Hydrogen Storage and Materials ; Layered Double Hydroxides Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Ijaz Ul Haq | Seung Jun Lee
Source: Applied Sciences (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Unveiling State-of-Charge Effects on Elastic Properties of LiCoO2 via Deep Learning and Empirical Models This study investigates the mechanical properties of LiCoO2 (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in Young’s modulus with decreasing SOC. Analysis of stress factors identified pairwise interactions, particularly those involving Co3+ and Co4+, as key drivers of this mechanical evolution. The DP model demonstrated superior performance by providing consistent and reliable predictions reflected in a smooth and monotonic stiffness decrease with SOC, in contrast to the large fluctuations observed in the classical Buckingham potential results. The study further identifies the increasing dominance of Co4+ interactions at low SOCs as a contributor to localized stress concentrations, which may accelerate crack initiation and mechanical degradation. These findings underscore the DP model’s capability to capture SOC-dependent mechanical behavior accurately, establishing it as a robust tool for modeling battery materials. Moreover, the calculated SOC-dependent mechanical properties can serve as critical input for continuum-scale models, improving their predictive capability for chemo-mechanical behavior and degradation processes. This integrated multiscale modeling approach can offer valuable insights for developing strategies to enhance the durability and performance of lithium-ion battery materials. Charge (physics) ; Materials science ; Statistical physics ; Psychology ; Econometrics ; Artificial intelligence ; Computer science ; Economics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Robert J. Appleton | Brian C. Barnes | Alejandro Strachan
Source: Journal of Chemical Information and Modeling (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Data Fusion of Deep Learned Molecular Embeddings for Property Prediction Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on the strength of the underlying correlations between tasks and the completeness of the data set. Standard multitask models tend to underperform when trained on sparse data sets with weakly correlated properties. To address this gap, we fuse deep-learned embeddings generated by independent pretrained single-task models, resulting in a multitask model that inherits rich, property-specific representations. By reusing (rather than retraining) these embeddings, the resulting fused model outperforms standard multitask models and can be extended with fewer trainable parameters. We demonstrate this technique on a widely used benchmark data set of quantum chemistry data for small molecules as well as a newly compiled sparse data set of experimental data collected from literature and our own quantum chemistry and thermochemical calculations. Benchmark (surveying) ; Multi-task learning ; Property (philosophy) ; Set (abstract data type) ; Data set ; Transfer of learning ; Deep learning ; Fuse (electrical) ; Limiting ; Sensor fusion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Taiko Ohshida | Toru Wada | Taiyo Kaneuchi | Emika Ohno | Toshiaki Taniike
Source: Polymer (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Data-driven phase analysis of immiscible polymer blends via multi-parameter AFM imaging Polyolefin ; Polymer blend ; Ternary operation ; Polymer ; Phase (matter) ; Atomic force microscopy ; Microscopy Thermal properties of materials ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Shuang Zhou | Qian Lei | Jie Yin | Xiang Yan | Xiukuang Zhang | Wenwen Liu
Source: JOM (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Machine Learning Inverse Prediction of Cu-Zn-Al Alloy’s Composition and Target Properties Using a Two-Way Feedback Structure on a Small Data Set Alloy ; Inverse ; Composition (language) ; Set (abstract data type) ; Metallurgy ; Materials science ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: Ming‐Qiang Qi | Ruiwu Yang | Jianyu Wu | Jianan Chen | Yulin Qi | X. Diao | Ming‐Hao Du | Yibin Jiang | Cheng Wang | La‐Sheng Long | Lan‐Sun Zheng | Xiang‐Jian Kong
Source: Angewandte Chemie International Edition (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Assembly of a Nested Gd<sub>20</sub>@Gd<sub>32</sub> @Ni<sub>36</sub> Cluster via Urea Controlled Carbonate Release Lanthanide-based polyhedral clusters are of great interest due to their unique geometries and functional properties, but their controlled synthesis remains a major challenge. Here, we report a nested three-shell cluster, [Gd<sub>52</sub>Ni<sub>36</sub>(MeIDA)<sub>36</sub>(OH)<sub>114</sub>(CO<sub>3</sub>)<sub>12</sub> (H<sub>2</sub>O)<sub>48</sub>]·(ClO<sub>4</sub>)<sub>18</sub>·(H<sub>2</sub>O)<sub>21</sub> (Gd<sub>52</sub>Ni<sub>36</sub>, H<sub>2</sub>MeIDA = N-Methyliminodiacetic acid), achieved by using urea as a slow-release carbonate source to direct the formation of a dodecahedral inner shell. Single-crystal X-ray diffraction reveals a unique Gd<sub>20</sub>@Gd<sub>32</sub>@Ni<sub>36</sub> arrangement, with the innermost Gd<sub>20</sub> forming a Platonic dodecahedron templated by carbonate. A machine learning-guided, high-throughput synthesis platform enabled the exploration of 780 reaction conditions, uncovering key parameters and phase boundaries governing cluster formation. This work demonstrates data-driven strategies can accelerate the discovery of complex lanthanide architectures. Dodecahedron ; Lanthanide ; Cluster (spacecraft) ; Carbonate ; Materials science ; Crystallography ; Chemistry ; Ion ; Computer science ; Metallurgy Nanocluster Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Yinhe Wang | Chao Wang | Li-Hui Mou | Jun Jiang
Source: JACS Au (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Deciphering Experimental Reactivity of Metal Clusters Toward N<sub>2</sub> Activation Using Graph Neural Networks Machine learning (ML) analysis of gas-phase metal cluster reactivity has emerged as a pivotal approach in this field. However, existing ML studies relying on electronic properties have primarily focused on discrete features, with less consideration of continuous structural factors that also govern cluster reactivity. Here, we present the first graph neural network (GNN) framework to model N<sub>2</sub> activation reactivity across 245 metal clusters, combining DFT-optimized structures with experimental reaction rates collected from the literature and a public data set. Through encoding both topological connectivity and atomic-level features (e.g., natural charge, valence electron occupancy, and atomic number), the graph isomorphism network (GIN) achieves superior predictive performance on reaction rates of unseen clusters. Explainable analysis reveals that natural charge redistribution likely serves as the primary mechanism for ligand-mediated reactivity modulation. Furthermore, subgraph charge polarization shows potential as a reactivity descriptormetal-core subgraphs in highly active clusters exhibit significantly lower charge polarization than mixed metal-ligand subgraphs in less active clusters. This work establishes a graph-based interpretable framework for understanding structure-activity relationships of small-molecule activation by metal clusters. Reactivity (psychology) ; Graph ; Artificial neural network ; Metal ; Computer science ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Medicine ; Organic chemistry Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalytic Processes in Materials Science
Authors: Thiago H. da Silva | Jiang Lu | Zayah Cortright | Denis Mulumba | Md Sharif Khan | Oliviero Andreussi
Source: Journal of Chemical Information and Modeling (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Automating the Analysis of Substrate Reactivity through Environment Interaction Mapping Exploring the interaction configurations between substrates and atomic or molecular systems is crucial for various scientific and technological applications, such as characterizing catalytic reactions, solvation structures, and molecular interactions. Traditional approaches for generating substrate-reactant configurations often rely on chemical intuition, symmetry operations, or random initial states, which can be inefficient and challenging for systems with low symmetry or unknown interaction mechanisms. This work introduces a systematic and automated methodology to explore the configuration space between substrates and adsorbates using symmetry-invariant features that characterize the local atomistic topology of the substrate. The approach involves three key components: (1) defining and discretizing a contact space surrounding the substrate, (2) utilizing symmetry-invariant descriptors to capture local atomic environments, and (3) employing unsupervised machine-learning techniques for clustering and hierarchical analysis of interaction sites. The method ensures comprehensive yet nonredundant sampling of the configuration space, independent of the substrate dimensionality. Applications to simple ideal substrates show that symmetry intuition and high-symmetry sites are correctly recovered. Moreover, the method is shown to translate seamlessly to less symmetric substrates. Reactivity (psychology) ; Substrate (aquarium) ; Computer science ; Computational biology ; Chemistry ; Biology ; Medicine ; Ecology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wei Zhang | Weiyi Xia | Qing Tian | Jian Lv | Cai‐Zhuang Wang
Source: Journal of Alloys and Compounds (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Machine learning guided search for energetically favorable metal borocarbide ternary compounds Ternary operation ; Metastability ; Metal ; Cluster (spacecraft) ; Group (periodic table) ; Crystal structure prediction Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; MXene and MAX Phase Materials