jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weiyi Li | Xiyao Yun | GU Xiao-hui | Rong Liu | Yilin Fang | Wanxiao Guo | Jintao Wang | Tao Wang | Ning Gao
Source: Journal of Chemical Information and Modeling (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

HlightReaxMD: A Machine Learning-Augmented Multiscale Analysis Framework for Radiation Chemistry Dynamics and Damage Prediction Molecular dynamics (MD) simulations are currently widely used to study large-scale displacement cascades based on massive simulation trajectories. However, when the irradiation process involves the complex chemical reactions, effectively analyzing and extracting features from these data becomes a challenge. Here, we introduce a new cross-platform toolkit, HlightReaxMD, designed to directly obtain information about the irradiation damage process and chemical reactions from MD trajectories and further achieve the prediction of irradiation damage. The analysis tools in HlightReaxMD include chemical reaction analysis and calculation of reaction kinetic parameters, analysis of the collision cascade process, and calculation of necessary physicochemical properties. HlightReaxMD supports the analysis of all elements used in reactive force fields by reading ReaxFF potential file parameters and provides an automated solution for tracking atomic-scale collision events and analyzing chemical reaction mechanisms through constructing cascade trees and reaction network paths. A machine learning-driven model using the analysis results has been included in HlightReaxMD, which can predict irradiation damage by considering various factors, rather than relying solely on the Norgett-Robinson-Torrens displacements per atom (NRT-dpa) model. It enables researchers to automatically obtain dynamic processes and reaction information from atomic to microscale defects from terabyte-level trajectory data. Thus, HlightReaxMD can promote systematic research on irradiation effects in materials science. ReaxFF ; Microscale chemistry ; Molecular dynamics ; Cascade ; Chemical reaction ; Process (computing) ; Trajectory ; Irradiation ; Collision Machine Learning in Materials Science ; Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Mingjie Wen | Jun Han | Xiaohong Zhang | Yuzeng Zhao | Yan Zhang | Dongping Chen | Qingzhao Chu
Source: Chemical Engineering Science (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Predicting the catalytic mechanisms of CuO/PbO on energetic materials using machine learning interatomic potentials Interatomic potential ; Catalysis ; Materials science ; Thermodynamics ; Physical chemistry ; Chemical engineering ; Chemistry ; Computational chemistry ; Physics ; Engineering ; Molecular dynamics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Victor Rodrigues | Víctor de Souza Assumção Bonfim | Demétrio A. da Silva Filho
Source: Journal of Molecular Modeling (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of organic solar cell performance: a data-centric approach to molecular design Hyperparameter ; Cheminformatics ; Fullerene ; Feature (linguistics) ; Acceptor ; Artificial neural network ; Organic solar cell ; Variety (cybernetics) Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Ricky Tran | Valerie Brunskill | Amanda Musgrove | Todd C. Sutherland | Darren J. Derksen
Source: Journal of Chemical Education (article, 2025-07-10)
Field: Chemistry — Physical and Theoretical Chemistry

A Problem-Based Introduction to Machine Learning in the Undergraduate Organic Chemistry Laboratory: Prediction of Diels–Alder Reaction Rates To engage students in higher-order thinking skills, an inquiry-based dry laboratory experience was developed for upper-year undergraduate students, where students were introduced to machine learning approaches to solve chemical problems. Students constructed their own data set of diene and dienophile features and performed a multivariate linear regression in a Python environment to predict the energy barriers (ΔG‡) of a Diels–Alder system. They applied their models to a simulated drug development problem. Likert-scale surveys and qualitative interviews were utilized to collect data on student experiences. Students expressed that they felt strongly engaged in critical and creative thinking, collaboration, and metacognition. Subsequently, students felt that computational tools were more approachable, and had a stronger appreciation of how computational tools could be utilized in chemistry contexts. Students also expressed that they felt the freedom to make mistakes, reflect, and improve, embracing a growth mindset in this laboratory. Diels–Alder reaction ; Alder ; Chemistry ; Computer science ; Organic chemistry ; Mathematics education ; Mathematics ; Ecology ; Biology ; Catalysis Various Chemistry Research Topics ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Minglei Zhang | Xiaoya Chen | Quanan Li | Zheng Wu | Jiaqi Xie
Source: Metals and Materials International (article, 2025-03-17)
Field: Materials Science — Biomaterials

Machine Learning-Assisted Efficient Design of Mg–Gd–Y Based System Alloys Materials science ; Solid mechanics ; Systems engineering ; Metallurgy ; Chemical engineering ; Composite material ; Engineering Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: David Graff | Nathan K. Morgan | Jackson Burns | Anna C. Doner | Brian Li | Shih‐Cheng Li | Joel Manu | Angiras Menon | Hao‐Wei Pang | Haoyang Wu | Akshat Shirish Zalte | Jonathan W. Zheng | Connor W. Coley | William H. Jr. Green | Kevin P. Greenman
Source: Journal of Chemical Information and Modeling (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction Accurate prediction of molecular properties is essential for computational design in many areas of chemistry. Deep learning has been used in these prediction tasks for a wide variety of molecular properties, and the availability of user-friendly open-source software implementing such architectures has democratized access to these methods. chemprop is one of the most popular examples of such software in this field. It implements a directed message-passing neural network (D-MPNN) architecture, enabling end-to-end learning of molecular properties directly from molecular graphs without the need for handcrafted descriptors or fingerprints. The original chemprop release was intended for use primarily via a command line interface, rather than programmatic use via a Python API. As the field has evolved, the need for increased modularity and usability in Python-based workflows has become clear. We completed a ground-up rewrite of chemprop that addresses this need, providing improvements in speed, extensibility, and overall user experience. We have conducted extensive benchmarking to demonstrate algorithmic parity with the original implementation, while seeing improvements of about a factor of 2 in execution time and a factor of 3 in memory usage. chemprop v2 effectively scales to multiple GPUs, which enables the training of more and larger models. chemprop v2 also includes some new features. Extensive Jupyter notebook tutorials and new documentation for all major functionality were also added. chemprop v2 preserves the predictive accuracy of its predecessor and enhances modularity, speed, and usability, empowering researchers to pursue computational molecular design more effectively. Workflow ; Python (programming language) ; Usability ; Benchmarking ; Documentation ; Modular design ; Software ; Field (mathematics) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kirill Kulaev | Alexander Ryabov | Michael G. Medvedev | Evgeny Burnaev | Vladimir Vanovskiy
Source: The Journal of Chemical Physics (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization Density functional theory is the workhorse of present-day quantum chemistry thanks to its good balance between calculation accuracy and speed. In recent years, several neural network-based exchange-correlation functionals have been developed, with DM21, developed by Google DeepMind, being the most recognizable among them. In this study, we focus on evaluating the efficiency of DM21 functional on the task of optimizing molecular geometries and investigate how the non-smooth behavior of neural network-predicted exchange-correlation energy and potential affects the final geometry precision. We implement geometry optimization for the DM21 functional in PySCF and compare its performance with traditional functionals on various benchmarks. Our findings reveal that numerical noise coming from the neural network outputs contaminates numerical nuclear gradients required for geometry optimization. We also found that a numerical differentiation step in the range of 0.0001-0.001 Å is required to obtain sufficiently smooth nuclear gradients. Furthermore, we show that the non-smoothness of DM21 can be reproduced by adding random normally distributed noise to local energies of an analytical SCAN functional, allowing one to efficiently estimate the optimal numerical differentiation step for geometry optimization of a given molecule. Our findings show that DM21 does not outperform analytical functionals in the accuracy of optimized molecular geometries and is significantly slower, which limits its practical applicability to chemical calculations. Energy minimization ; Smoothness ; Artificial neural network ; Density functional theory ; Focus (optics) ; Computer science ; Hybrid functional ; Stochastic geometry ; Algorithm ; Noise (video) ; Geometry ; Range (aeronautics) ; Mathematics ; Physics ; Artificial intelligence ; Image (mathematics) ; Mathematical analysis ; Materials science ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Debojyoti Das | Erico S. Teixeira | Jorge A. Morales
Source: Journal of Computational Chemistry (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Recurrent Neural Network/Machine Learning Predictions of Reactive Channels in H<sup>+</sup> + <scp>C<sub>2</sub>H<sub>4</sub></scp> at <i>E</i><scp><sub>Lab</sub></scp> = 30 <scp>eV</scp>: A Prototype of Ion Cancer Therapy Reactions We present a simplest-level electron nuclear dynamics/machine learning (SLEND/ML) approach to predict chemical properties in ion cancer therapy (ICT) reactions. SLEND is a time-dependent, variational, on-the-fly, and nonadiabatic method. In SLEND, nuclear and electronic parameters determine reactants-to-products trajectories in a quantum phase space; this establishes a mapping between reactants' initial conditions and products' properties. To accelerate simulations, SLEND/ML utilizes a modicum of SLEND trajectories to train ML methods on the aforesaid mapping and employs them to predict chemical properties. We employ SLEND/ML to predict reaction types and products' charges in H<sup>+</sup> + C<sub>2</sub>H<sub>4</sub> at E<sub>Lab</sub> = 30 eV, a prototype of ICT reactions involving double-bonded compounds. For reaction predictions, a recurrent neural network (RNN) and k-nearest neighbor method are the best models with 98.23% and 95.13% accuracy. RNN correctly predicts frequent and infrequent reaction types and generalizes over data sets. For charge predictions, the RNN exhibits low mean absolute errors of 0.02-0.07. Recurrent neural network ; Artificial neural network ; Phase space ; Chemistry ; Physics ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Yuqing Sun | Xiaorui Chen | Jianzhi Gao | Wenliang Zhu | Minghu Pan
Source: Advanced Electronic Materials (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Rationally Design Thermoelectric Materials Based on Ingenious Machine Learning Methods Abstract Data quality, feature interpretability, and model generalization are critical and challenging for applying machine learning (ML) in the design of high‐efficiency materials. In this work, an ML framework with integrating multi‐step feature engineering is constructed for predicting the figure of merit ( ZT ) values of thermoelectric materials. By incorporating thermoelectric material data from the Starrydata2 database and implementing rigorous data cleaning, a high‐quality ZT prediction dataset is established. An integrated strategy of feature extraction with combining Magpie and CBFV methods is utilized, followed by feature selection via Pearson correlation analysis and LassoCV cross‐validation. Finally, the deep neural network model (Model‐I) demonstrates excellent predictive performance ( R 2 = 0.95 on the training set and R 2 = 0.90 on the test set), as well as identified successfully promising candidates such as CsCdBr 3 and TlBSe 3 in screening chalcogenide and halide perovskites. Combined with Density Functional Theory (DFT) calculation, the outstanding thermoelectric performance of CsCdBr 3 under p‐type doping ( ZT max = 1.64) and the bipolar thermoelectric characteristics of TlBSe 3 ( ZT max = 1.04 for n‐type and ZT max = 0.99 for p‐type) at 800K are successfully demonstrated, further confirming the reliability of our method. This study provides an applicative data‐driven approach for functional material design, balancing predictive accuracy and physical interpretability. Materials science ; Thermoelectric effect ; Thermoelectric materials ; Nanotechnology ; Process engineering ; Systems engineering ; Mechanical engineering ; Composite material ; Engineering ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Henrietta Homm | Jarno Laakso | Patrick Rinke
Source: Physical Review Materials (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Efficient dataset generation for machine learning halide perovskite alloys Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can accelerate property predictions for the $\mathrm{CsPb}{(\mathrm{Cl}/\mathrm{Br})}_{3}$ perovskite alloy. However, the substantial computational demand of density functional theory (DFT) calculations required for model training prevents applications to more complex materials. Here, we introduce a data-efficient scheme to facilitate model training, validated initially on $\mathrm{CsPb}{(\mathrm{Cl}/\mathrm{Br})}_{3}$ data and extended to the ternary alloy $\mathrm{CsSn}{(\mathrm{Cl}/\mathrm{Br}/\mathrm{I})}_{3}$. Our approach employs clustering to construct a compact yet diverse initial dataset of atomic structures. We then apply a two-stage active learning approach to first improve the reliability of the ML-based structure relaxations and then refine accuracy near equilibrium structures. Tests for $\mathrm{CsPb}{(\mathrm{Cl}/\mathrm{Br})}_{3}$ demonstrate that our scheme reduces the number of required DFT calculations during the different parts of our proposed model training method by up to 20% and 50%. The fitted model for $\mathrm{CsSn}{(\mathrm{Cl}/\mathrm{Br}/\mathrm{I})}_{3}$ is robust and highly accurate, evidenced by the convergence of all ML-based structure relaxations in our tests and an average relaxation error of only 0.5 meV/atom. Materials science ; Perovskite (structure) ; Halide ; Engineering physics ; Optoelectronics ; Chemical engineering ; Inorganic chemistry Machine Learning in Materials Science ; Machine Learning and ELM ; Electronic and Structural Properties of Oxides
Authors: Chang Cun Tu | Xin Li | Junmin Chen | Bo Sun | Kuang Yu
Source: The Journal of Physical Chemistry B (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Enhancing Thermal Conductivity Computation of Polymers via Machine Learning Techniques Accurate prediction of the thermal conductivity (κ) of polymers is generally challenging due to their complex structures. Currently available ab initio methods (e.g., DFT-BTE) are prohibitively expensive, and the classical force fields used in molecular dynamics lack accuracy. In this study, we combine ab initio hybrid machine learning (ML)/multipolar polarizable potential (i.e., PhyNEO) with ML-facilitated heat flux calculation. This approach provides reliable heat flux trajectories, which are then used to predict polymer κ quantitatively. Using poly(ethylene oxide) as an example, we compare our calculation results with reliable experimental reference obtained from time-domain thermoreflectance measurement, reaching excellent agreement. This work enables the quantitative prediction of bulk polymer κ starting from only small cluster quantum data, warranting broad applications in future. Polarizability ; Ab initio ; Thermal conductivity ; Polymer ; Work (physics) ; Computation ; Materials science ; Experimental data ; Molecular dynamics ; Thermal conduction ; Heat flux ; Conductivity ; Thermal ; Statistical physics ; Computer science ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Algorithm ; Heat transfer ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Matthew S. Johnson | David H. Bross | Judit Zádor
Source: The Journal of Physical Chemistry C (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows Microkinetic models for catalytic systems require estimation of many thermodynamic and kinetic parameters that can be calculated for isolated species and transition states using ab initio methods. However, the presence of nearby coadsorbates on the surface can dramatically alter these thermodynamic and kinetic parameters causing them to be dependent on species coverage fractions. As there are combinatorially many coadsorbed configurations on the surface, computing the coverage dependence of these parameters is far less straightforward. We present a framework for generating and applying machine learning models to predict coverage-dependent parameters for microkinetic models. Our toolkit enables automatic calculation and evaluation of coadsorbed configurations allowing us to sample 2,000 coadsorbed adsorbates and transition states (TSs) for a diverse set of 9 reactions on Cu(111), a challenging surface, with four possible coadsorbates. This dataset was then used to train subgraph isomorphic decision trees (SIDTs) to predict the stability and association energy of configurations. We were able to achieve mean absolute errors (MAEs) of 0.106 eV on adsorbates, 0.172 eV on TSs, and due to natural error cancellation in SIDTs for relative properties, 0.130 eV on reaction energies and 0.180 eV on activation barriers. We describe how to use these models to predict coverage-dependent corrections for adsorbates and TSs and demonstrate on H*, HO*, and O* comparing the generated SIDT model with an iteratively refined version. Workflow ; Kinetics ; Quantum chemistry ; Surface (topology) ; Computer science ; Quantum ; Quantum dot ; Chemistry ; Nanotechnology ; Materials science ; Reaction mechanism ; Physics ; Mathematics ; Database ; Catalysis ; Organic chemistry ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Yuming Gu | Yating Gu | Maochen Yang | Shisi Tang | Jiawei Chen | Xinyi Liang | Zheng Dong | Zekun Li | Fengqi Song | Yang Gao | Yan Zhu | Yinghuan Shi | Jing Ma
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-throughput design of bimetallic materials <i>via</i> multimodal machine learning and the accessibility index It is a great challenge to efficiently explore bimetallic systems containing miscible or immiscible elements (<i>e.g.</i>, Au/Ni and Au/Rh) due to the difficulty in screening candidates with favorable formation energy (<i>E</i> <sub>form</sub>) from the vast combination space of different metal pairs and ligands or coordination environments. The importance of the coordination environment is highlighted through the multilevel attention mechanism within the graph convolutional neural network (GCNN) and the Shapley additive explanation (SHAP) analysis for an 8-feature scheme in <i>E</i> <sub>form</sub> prediction. To further reduce the prediction error of formation energy in the test set, multimodal machine learning (MML) is applied to 11 186 bimetallic nanocluster configurations by integrating the molecule graph of the metal core and the physical property features such as mixing enthalpy (<i>H</i> <sub>mix</sub>) of the bimetallic pair and SMILES strings and solubility (log <i>P</i>) of the ligand. The present MML model could predict nanoclusters with up to more than one thousand atoms rapidly. To evaluate the experimental accessibility of bimetallic porous materials, alloys, and 2D materials in a general way, an accessibility index, <i>φ</i>, is defined as the combination of the electronegativity (<i>χ</i> <sub>env</sub>) and the reduced atomic distance index <i>D̃</i> without the need for density functional theory (DFT) calculations. Larger values of <i>φ</i> indicate that the bimetallic materials are more accessible, owing to the energetically favorable interatomic charge transfer and optimal reduced distance around 0.3 (∼3.5 Å metal-metal distance) for nanoclusters and 0.1 (∼2.5 Å) for zeolites, respectively. Among the 100 external test samples, three nanoclusters (Au<sub>36</sub>Ag<sub>38</sub>((CF<sub>3</sub>)<sub>2</sub>PhC[triple bond, length as m-dash]C)<sub>30</sub>Cl<sub>10</sub>, Au<sub>38</sub>Ag<sub>33</sub>((CF<sub>3</sub>)<sub>2</sub>PhC[triple bond, length as m-dash]C)<sub>30</sub>Cl<sub>8</sub>, and Au<sub>9</sub>AgRh(PPh<sub>3</sub>)<sub>8</sub>Cl) and three 2D materials (Au/Ni@NC, Ni/Pt@NC, and Cu/Gd@NC) were synthesized in this work, in good agreement with that their accessibility indices (<i>φ</i>) are in the favorable range (<i>φ</i> ≥ 0.30) and low formation energies below -1 eV per atom. The proposed MML scheme and accessibility index hold promise in facilitating the high-throughput discovery and bimetallic material design. Bimetallic strip ; Nanoclusters ; Electronegativity ; Convolutional neural network ; Artificial neural network ; Density functional theory ; Metal ; Atomic radius Machine Learning in Materials Science