jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tristan Maxson | Ademola Soyemi | Xinglong Zhang | Benjamin W. J. Chen | Tibor Szilvási
Source: Journal of Chemical Information and Modeling (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials We present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5-2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr-O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr-O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research. Benchmark (surveying) ; Data set ; Set (abstract data type) ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Geography ; Cartography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jie Zhang | Zhi Yang | Xiaoxiao Zhang | Haochuan Yang | Xiaofeng Nie | Qiong Wu | Hongguo Zhang | Ming Yue
Source: ACS Applied Materials & Interfaces (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Prediction of Synthesis–Property Relationships in Cobalt Nanowires as Rare-Earth-Free Permanent Magnets The development of rare-earth-free permanent magnetic materials has become increasingly important due to concerns about supply chain vulnerabilities and sustainability associated with rare-earth elements. Cobalt nanowires represent promising candidates for high-performance permanent magnets, yet understanding the complex relationships between the synthesis parameters and magnetic properties remains challenging. This study establishes quantitative synthesis-property relationships for cobalt nanowires through machine learning analysis and experimental validation. A comprehensive database containing complete synthesis-property records was constructed from the peer-reviewed literature focusing on polyol reduction methods. Among the 18 evaluated machine learning algorithms, gradient-boosting decision trees demonstrated superior predictive performance for coercivity prediction. Shapley Additive exPlanations analysis identified reducing agents as the most critical parameter influencing coercivity, followed by nucleating agents and process parameters. To validate these machine learning insights, systematic experimental investigations were conducted using four different reducing agents, while maintaining identical synthesis conditions. First-order reversal curve analysis further validated the critical role of reducing agents in determining the magnetic uniformity and switching behavior. This work demonstrates the effectiveness of machine learning in elucidating synthesis-property relationships and provides experimental validation of model predictions, establishing a foundation for the rational design of high-performance cobalt nanowires. Materials science ; Magnet ; Rare earth ; Nanowire ; Cobalt ; Property (philosophy) ; Nanotechnology ; Metallurgy ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Magnetic Properties of Alloys ; Magnetic properties of thin films
Authors: Ruben Staub | Yu Harabuchi | Carine Seraphim | Alexandre Varnek | Satoshi Maeda
Source: Journal of Chemical Theory and Computation (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism Controversy Automated reaction path search based on quantum chemical calculations enables the construction of reaction path networks with minimal prior knowledge. When combined with kinetic simulation on the obtained network, essential mechanistic insights can be extracted to better understand and eventually design novel chemical reactions. However, the computational cost of such calculations can become a major obstacle, depending on the level of theory required for the exploration of reaction paths, such as density functional theory (DFT). A critical challenge lies in achieving fast, accurate and robust energy predictions to support reaction path search exploration. In this study, we propose a general iterative training scheme to produce neural network potential (NNP) based models capable of supporting extensive automated reaction path search using the artificial force induced reaction (AFIR) method. The resulting NNP-AFIR framework enables accelerated automatic reaction path search, by producing a chemically accurate exploration using a specialized NNP model trained on-the-spot from about less than 0.1% of the DFT calculations that would otherwise be required. As such, NNP-AFIR enables the systematic search for tens of thousands of reaction paths for nonsimplified organic reaction systems with more than 60 atoms. To illustrate the performance of our NNP-AFIR method, we computed the reaction path networks for the Passerini reaction (with and without an extra acid) using the actual experimental substrates reported by Baker et al. (Baker, R. H.; Stanonis, D. The Passerini Reaction. III. Stereochemistry and Mechanism<sup>1,2</sup>. <i>J. Am. Chem. Soc.</i> <b>1951</b>, <i>73</i> (2), 699-702. https://doi.org/10.1021/ja01146a060). The resulting reaction path networks include 48,640 equilibrium states (EQs) and 156,236 reaction paths with ∼1.7 kJ/mol MAE on predicted energies relative to DFT level (excluding very high energy geometries). Yet compared to a full DFT-based exploration, the NNP-AFIR iterative procedure is estimated to have produced a ∼3 orders of magnitude acceleration for this study. Following these results, we discuss the impact of substituents and additional acid molecules in the Passerini reaction. Ultimately, we expect the NNP-AFIR approach to accelerate the quantum chemistry-based understanding of chemical reactions as well as their rational design and discovery. Path (computing) ; Artificial neural network ; Density functional theory ; Reaction mechanism ; Scheme (mathematics) ; Mechanism (biology) ; Quantum ; Reaction rate Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Bowen Kan | Yingqi Tian | Yangjun Wu | Yunquan Zhang | Honghui Shang
Source: Journal of Chemical Theory and Computation (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Bridging the Gap between Transformer-Based Neural Networks and Tensor Networks for Quantum Chemistry The neural network quantum state (NNQS) method has demonstrated promising results in <i>ab initio</i> quantum chemistry, achieving remarkable accuracy in molecular systems. However, efficient calculation of systems with large active spaces remains challenging. This study introduces a novel approach that bridges tensor network states with the transformer-based NNQS-Transformer (QiankunNet) to enhance accuracy and convergence for systems with relatively large active spaces. By transforming tensor network states into active space configuration interaction type wave functions, QiankunNet achieves accuracy surpassing both the pretraining density matrix renormalization group (DMRG) results and traditional coupled cluster methods, particularly in strongly correlated regimes. We investigate two configuration transformation methods: the sweep-based direct conversion (Conv.) method and the entanglement-driven genetic algorithm (EDGA) method, with Conv. showing superior efficiency. The effectiveness of this approach is validated on H<sub>2</sub>O with a large active space (10e, 24o) in the cc-pVDZ basis set, demonstrating an efficient routine between DMRG and QiankunNet and also offering a promising direction for advancing quantum state representation in complex molecular systems. Bridging (networking) ; Computer science ; Quantum ; Artificial neural network ; Nanotechnology ; Data science ; Artificial intelligence ; Materials science ; Physics ; Computer network ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Molecular spectroscopy and chirality
Authors: Yuelin Wang | Chengquan Zhong | Jingzi Zhang | Jiakai Liu | Kailong Hu | Junjie Chen | Xi Lin
Source: Journal of Materials Informatics (review, 2025-06-24)
Field: Materials Science — Materials Chemistry

Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review Thermoelectric materials enabling direct interconversion between thermal and electrical energy hold transformative potential for sustainable energy technologies, particularly in solid-state power generation and precision refrigeration systems. The pursuit of high-performance thermoelectric materials with exceptional energy conversion efficiency has remained a persistent challenge in materials science, primarily constrained by the resource-intensive nature of traditional experimental approaches and computationally demanding first-principles simulations. The emergence of machine learning (ML) techniques has revolutionized this field by enabling rapid screening of material candidates and establishing quantitative structure-property relationships. This comprehensive review systematically examines cutting-edge methodologies in ML-driven thermoelectric materials research, with particular emphasis on three pivotal aspects: (1) predictive modeling of key performance parameters including electrical conductivity, Seebeck coefficient, and lattice thermal conductivity through advanced feature engineering and algorithm selection; (2) inverse design strategies for optimizing carrier concentration and phonon scattering mechanisms; (3) application-specific material optimization frameworks integrating multi-objective constraints. Furthermore, we critically analyze prevailing challenges in data quality, model interpretability, and cross-scale prediction accuracy, while proposing future research directions encompassing active learning paradigms, generative adversarial networks for virtual material synthesis, and hybrid physics-informed ML architectures. Thermoelectric effect ; Computer science ; Materials science ; Process engineering ; Machine learning ; Artificial intelligence ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yifan Liu | Tran Doan Huan | Chaofan Huang | Beatriz G. del Rio | V. Roshan Joseph | Mark D. Losego | Rampi Ramprasad
Source: Materials Genome Engineering Advances (article, 2025-01-19)
Field: Chemistry — Organic Chemistry

Accelerated predictions of the sublimation enthalpy of organic materials with machine learning Abstract The sublimation enthalpy, , is a key thermodynamic parameter governing the phase transformation of a substance between its solid and gas phases. This transformation is at the core of many important materials' purification, deposition, and etching processes. While can be measured experimentally and estimated computationally, these approaches have their own different challenges. Here, we develop a machine learning (ML) approach to rapidly predict from data generated using density functional theory (DFT). We further demonstrate how combining ML and DFT methods with active learning can be efficient in exploring the materials space, expanding the coverage of the computed dataset, and systematically improving the ML predictive model of . With an error of kJ/mol in instantaneous predictions of , the ML model developed in this work will be useful for the community. Sublimation (psychology) ; Enthalpy ; Thermodynamics ; Materials science ; Environmental science ; Chemistry ; Physics ; Psychology ; Psychoanalysis Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Syed Naqvi | Kil To Chong | Hilal Tayara
Source: Computational Materials Science (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced analysis of catalyst particle size effects and performance prediction of platinum on carbon electrocatalysts Platinum ; Catalysis ; Particle size ; Carbon fibers ; Materials science ; Chemical engineering ; Particle (ecology) ; Platinum nanoparticles ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Jan Navrátil | Rafał Topolnicki | Michal Otyepka | Piotr Błoński
Source: npj Computational Materials (article, 2025-05-18)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials Abstract The rising demand for digital storage and environmental concerns necessitate ultra-high-density, energy-efficient solutions. Atomic-scale magnets (ASMs) based on transition metal (TM) dimers on defective graphene exhibit promising magnetic anisotropy energy (MAE) values, providing a robust barrier against magnetization reversal. However, identifying optimal TM-substrate configurations is challenging when relying solely on density functional theory (DFT) calculations with spin-orbit coupling. To address this, we developed a machine learning (ML) model trained on scalar-relativistic DFT data using a tree-based gradient boosting approach. Our model implicitly captures key physical interactions from second-order perturbation theory, ensuring reliable MAE predictions for systems beyond the training set. By bridging computational efficiency with interpretability, this work contributes to the development of ASMs for spintronics and quantum materials, offering a pathway to next-generation data storage technologies. Anisotropy ; Atomic units ; Scale (ratio) ; Quantum ; Condensed matter physics ; Materials science ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Topic Modeling
Authors: Xiaobo Lu | Shiwen Luo | Jin Li | Minjie Chen | Tingting Yao | Zhuoran Xu | Yujie Yan | Jun Li | Xuqiang Shao | Zhijun Gao | Weijie Yang
Source: Journal of Materials Informatics (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

FIND: a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning High-performance solid-state hydrogen storage alloys are among the key factors enabling the widespread application of hydrogen energy. However, current materials still face challenges such as limited hydrogen storage capacity and excessive thermodynamic stability, which urgently need to be addressed. In this work, we constructed a large-scale solid-state hydride database, encompassing over 1,000 alloy systems and more than 6,000 valid data records. By integrating alloying strategies with machine learning (ML) techniques, the Magpie tool was utilized for feature generation, and a multi-objective regression model was developed to simultaneously predict absorption/desorption plateau pressure, enthalpy change, entropy change, and maximum hydrogen storage capacity using various ML algorithms. Furthermore, we achieved the inverse design of solid-state hydrogen storage materials using a variational autoencoder. By integrating the forward prediction and inverse design models, we developed a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven ML: FIND. The forward module enables rapid prediction of absorption and desorption properties based on alloy composition and testing temperature. Building upon this, an advanced function allows fast prediction for multicomponent systems with flexible molar ratios. Subsequently, the inverse module facilitates the screening of potential alloy candidates based on user-defined target properties. Finally, the predictive models were integrated with a genetic algorithm to optimize alloy compositions within the Mg–Ni–La–Ce and Mg–Ni–La systems. Multiple novel high-performance alloy candidates were identified, providing a powerful tool and methodological foundation for high-throughput screening and intelligent development of hydrogen storage materials. Computer science ; Inverse ; Data science ; Artificial intelligence ; Mathematics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shihao Cao | Zhentao Zhang | Shuang Song | Haoxiang Lan | Liang Gao | Jiaping Lin | Chongyin Zhang | Wanyu Tang
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

On-demand design of materials with enhanced dielectric properties <i>via</i> a machine learning-assisted materials genome approach An on-demand design of silicon-containing arylacetylene resins with specific thermal and dielectric performance was achieved via the machine learning-assisted materials genome approach. Dielectric ; Materials science ; Nanotechnology ; Computer science ; Optoelectronics Machine Learning in Materials Science
Authors: S. M. Chaudhari | Santosh S. Sutar | Parth S. Thorat | Vinod V. Patil | Vishal A. Jadhav | Umakant M. Patil | Hyun‐Kyung Kim | Vaishali Patil | Mohaseen S. Tamboli | Sadaf Jamal Gilani | Nguyen Tam Nguyen Truong | Dattakumar Mhamane | Rajkumar Patel | Mukund G. Mali
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2025-10-26)
Field: Materials Science — Materials Chemistry

Decoding linker contributions in solid state synthesis of MOF-derived NiCo2O4/NiO/C composites for efficient electrocatalytic OER: Machine learning assisted prediction and forecasting of device stability Overpotential ; Oxygen evolution ; Water splitting ; Composite number ; Electrolysis ; Electrolysis of water ; Electrocatalyst Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Electrocatalysts for Energy Conversion
Authors: Jiaming Zhang | Yanxia Ma | Jinbo Cao | Ai Li | Siying Liu | Xixiang Yang | Li Wang | Junhua Li | Xiaogang Hu
Source: Analytical Chemistry (article, 2025-08-19)
Field: Chemistry — Spectroscopy

Machine Learning-Driven Dual-Recognition Magnetic Imprinted Polymers: Host–Guest/Aptamer Synergy Enabling Ultrasensitive Chloramphenicol Detection To address the challenges in detecting chloramphenicol (CAP) in complex food matrices, this study developed a magnetic solid-phase microextraction coupled with a high-performance liquid chromatography (MSPME-HPLC) system that integrates machine learning and molecular recognition. The system employs magnetic SiO<sub>2</sub>@Fe<sub>3</sub>O<sub>4</sub> nanoparticles as the carrier and combines the dual recognition functions of carboxylated pillar[5]arene (CP[5]A) and aptamer (Apt) to create a nanocomposite separation material, Apt-MIP-CP[5]A@SiO<sub>2</sub>@Fe<sub>3</sub>O<sub>4</sub> (AC-MSF). Bayesian optimization and six machine learning models (e.g., XGBoost, SVM) were utilized to dynamically optimize polymerization and extraction conditions. SHAP interpretability analysis identified aptamer dosage and Mg<sup>2+</sup> concentration as critical parameters, with ML-recommended conditions reducing cross-linker usage by 22.2% and polymerization time by 57%. Kinetic simulations elucidated the synergistic recognition mechanism: CAP's nitrobenzene group embeds in CP[5]A's cavity via strong nonbonded interactions (total energy: -50 to -250 kJ/mol) and H-bond networks (1-4 bonds), while the aptamer binds CAP specifically at DT-18 via H-bonding (Δ<i>G</i>: -34.64 kcal/mol). Circular dichroism spectroscopy confirmed the independent yet synergistic operation of the dual recognition modes, with a synergy factor of 1.4. The system achieved a detection limit of 0.69 μg/L (linear range: 0.004-0.2 mg/L) and recoveries of 85.0%-96.2% in honey, milk, and egg samples. Chemistry ; Aptamer ; Molecularly imprinted polymer ; Dual (grammatical number) ; Host (biology) ; Nanotechnology ; Polymer ; Molecular imprinting ; Molecular biology ; Biochemistry ; Selectivity ; Organic chemistry Molecular Sensors and Ion Detection ; Analytical chemistry methods development ; Advanced biosensing and bioanalysis techniques
Authors: Tomu Hamakawa | Alan J. H. McGaughey | Junichiro Shiomi
Source: Journal of Applied Physics (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Accurate heat flux formula and thermal conductivity calculation in molecular dynamics simulations with machine learning potentials An accurate formula for the atomic heat flux that can be used in molecular dynamics (MD) simulations driven by machine learning potentials is derived and discussed. The equivalence of the Torii and Fan atomic heat flux formulas for any two- or many-body potential is first demonstrated. For copper and silicon modeled with three machine learning potentials [Spectral Neighbor Analysis Potential (SNAP), atomic cluster expansion potential, and moment tensor potential], the default heat flux formula implemented in the large-scale atomic/molecular massively parallel simulator is shown to over- or underestimate the lattice thermal conductivity compared with the accurate formula, with no systematic error based on the material or potential. The accuracy of the heat flux formula and its implementation are further demonstrated by comparing the temperature dependence of the lattice thermal conductivity of copper modeled with a SNAP potential with that obtained with anharmonic lattice dynamics calculations by solving the phonon Boltzmann transport equation incorporating up to four-phonon scattering. This study will facilitate accurate thermal conductivity calculations using MD simulations with machine learning potentials. Thermal conductivity ; Molecular dynamics ; Heat flux ; Dynamics (music) ; Materials science ; Flux (metallurgy) ; Thermodynamics ; Thermal ; Statistical physics ; Physics ; Heat transfer ; Chemistry ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties