jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feng Wang | Yuhang Tang | Ze-Bing Ma | Yucheng Jin | Jun Cheng
Source: ChemRxiv (preprint, 2025-03-03)
Field: Materials Science — Materials Chemistry

Soaring Chemical Space of Battery Electrolytes Using Universal Machine Learning Potential Li-ion batteries, widely used in electronic devices, electric vehicles, and aviation, demand high energy density, fast charging capabilities, and broad operating temperature ranges. Computational approaches combined with experimental design have gained increasing attention for electrolyte development. However, the inherent complexity of electrolytes—arising from their diverse compositions and varying proportions—poses a significant challenge. Classical molecular dynamics often fails due to inaccuracies in force field parameters, while ab initio calculations are limited by prohibitive computational costs. Machine learning molecular dynamics (MLMD) offers a promising alternative, combining efficiency with ab initio accuracy. However, its potential has been hindered by the transferability limitations of machine learning potentials (MLPs). In this work, we develop a universal machine learning potential (uMLP) for electrolytes by generating randomly composed electrolytes and employing an iterative training approach to collect representative datasets, effectively overcoming these limitations. The uMLP enables accurate computation of key properties, including density, solvation structure, viscosity, ionic conductivity, and operating temperature range, for a broad range of electrolytes through MLMD simulations. Furthermore, coordination dynamics analysis of Li+ , by quantifying the coordination lifetime (¯τ), provides a direct, quantitative measure of solvation strength. Shorter ¯τ, indicative of weak solvation, correlates with faster ion transport and higher ionic conductivity. The uMLP for electrolytes facilitates the prediction and optimization of electrolyte properties, offering a powerful tool for electrolyte design. Battery (electricity) ; Space (punctuation) ; Chemical space ; Computer science ; Electrolyte ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Electrode ; Operating system Machine Learning in Materials Science ; Advanced Battery Technologies Research
Authors: Outhman Abbassi | Soumia Ziti
Source: Molecular Diversity (article, 2025-04-19)
Field: Materials Science — Materials Chemistry

QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction Computer science ; Deep learning ; Artificial intelligence ; Graph ; Quantum ; Quantum machine learning ; Machine learning ; Quantum computer ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Arnold D. Sison | Michael M.N.A. Quaynor | S. A. Keishana Navodye | G. T. Kasun Kalhara Gunasooriya
Source: ChemCatChem (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

Insights into Segregation and Aggregation in Dilute Atom Alloy Catalysts Using DFT and Machine Learning Abstract Dilute atom alloys (DAAs) are an important class of heterogeneous catalysts due to their ability to precisely tune the activity and selectivity of reactions. DAA catalysts typically consist of a small quantity of metal solute in a metal host. Key considerations in the stability of DAA catalysts are the segregation and aggregation energy. In this work, we report a systematic theoretical study of segregation and aggregation energies of DAA catalysts composed of 3d, 4d, and 5d transition metals. To investigate the nature of DAAs, we analyzed both Bader charge and density of states, as well as formation energies, to identify the most stable DAA configuration for a given alloy. We further applied regression‐based, tree‐based, and neural network machine learning (ML) models to gain physics‐based insights in predicting segregation and aggregation energies based on readily available atomic and bulk features. We found that the d‐band filling of the solute and host, nearest neighbor distance of the host, and d‐band width of the solute determine the segregation energy, whereas the Pauling electronegativity of the host and solute, nearest neighbor distance of the host, and cohesive energy of host determine aggregation energy. Our findings provide crucial insights for DAA catalyst design. Alloy ; Catalysis ; Atom (system on chip) ; Chemistry ; Materials science ; Chemical engineering ; Chemical physics ; Computer science ; Metallurgy ; Organic chemistry ; Engineering ; Parallel computing Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Kun Tang | Yujing Zhao | Lei Zhang | Jian Du | Qingwei Meng | Qilei Liu
Source: Journal of Chemical Theory and Computation (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Accelerating Transition State Search and Ligand Screening for Organometallic Catalysis with Reactive Machine Learning Potential Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations. In this work, a reactive machine learning potential (RMLP) model is developed to accelerate transition state optimizations and ligand screening for organometallic catalysis based on an automated transition state database construction method and a higher-order equivariant message passing neural network. In case studies involving the ethylene hydrogenation reaction catalyzed by organometallic catalysts, RMLP rapidly predicts potential energy surfaces along intrinsic reaction coordinate paths, achieving speeds nearly 3 orders of magnitude faster than those of rigorous quantum chemistry calculations. Meanwhile, it maintains comparable accuracy with a root-mean-square deviation of 0.307 Å for transition state geometries and a mean absolute error of 0.871 kJ·mol<sup>-1</sup> for reaction barriers on the external test set, significantly outperforming semiempirical quantum chemistry methods. Our RMLP model offers an effective alternative to both rigorous and semiempirical quantum chemistry approaches for rapid and precise transition state optimizations, facilitating high-throughput screening of advanced organometallic catalyst ligands. Catalysis ; Transition state ; Transition metal ; Organometallic chemistry ; Quantum ; Ligand (biochemistry) ; Quantum chemistry ; Group 2 organometallic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Nishant Shukla | Manashi Saikia | M. Deka
Source: Materials Today Physics (review, 2025-07-16)
Field: Materials Science — Materials Chemistry

A review on electrolyte innovation enabled by machine learning for energy storage applications Materials science ; Electrolyte ; Energy storage ; Engineering physics ; Nanotechnology ; Process engineering ; Thermodynamics ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and ELM
Authors: Andaç Batur Çolak
Source: Materials Chemistry and Physics (article, 2025-07-06)
Field: Materials Science — Materials Chemistry

Integrating experimental data with machine learning for optimizing solid electrolyte support materials in molten carbonate fuel cells Electrolyte ; Fuel cells ; Carbonate ; Materials science ; Chemical engineering ; Process engineering ; Computer science ; Metallurgy ; Engineering ; Chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Shuwen Zhang | Ya Gao | Wei Chen | Handong Wang | John Z. H. Zhang | Tong Zhu
Source: JACS Au (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy Accurate prediction of reaction energetics remains a fundamental challenge in computational chemistry, as conventional density functional theory (DFT) often fails to reconcile high accuracy with computational efficiency. Here, we introduce Deep post-Hartree-Fock (DeePHF), a machine learning framework that integrates neural networks with quantum mechanical descriptors to achieve CCSD-(T)-level precision while retaining the efficiency of DFT to solve the reaction problems. By establishing a direct mapping between the eigenvalues of local density matrices and high-level correlation energies, DeePHF circumvents the traditional accuracy-scalability tradeoff. Trained on a limited data set of small-molecule reactions, our model demonstrates superior performance across multiple benchmark data sets, exhibiting exceptional transferability. In fact, its accuracy even surpasses that of advanced double-hybrid functionals, all while maintaining O-(N<sup>3</sup>) scaling. DeePHF offers a promising pathway to bridge the gap between high-level quantum chemistry methods and the practical demands for scalable, accurate models in computational chemistry, and with further refinement, it is poised to make significant contributions to the advancement of chemical reaction modeling. Computer science ; Deep learning ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Jiwon Sun | Junho Song | Juo Kim | Jinyoung Jeong | Eunsong Kim | Joonchul Kim | Taehyun Park | Kyoungmin Min
Source: ACS Applied Materials & Interfaces (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Cation-Disordered High-Entropy Garnet Structures as Solid-State Electrolytes for All-Solid-State Batteries: Machine Learning-Driven Discovery Exploring the vast chemical space of high-entropy (HE) solid-state electrolytes (SSEs) has become a highly active area of battery studies owing to the exceptional performance of all-solid-state batteries (ASSBs) with higher energy density and improved safety. The compositional complexity and extensive chemical space inherent to HE SSEs pose significant challenges for their investigation via conventional methodologies such as experimental approaches and density functional theory (DFT) calculations. In this study, we propose a novel material screening methodology aimed at accelerating the exploration of promising HE SSEs while maintaining reasonable computational costs and efficiency. Specifically, we introduce a machine learning (ML)-based screening framework to evaluate 4348 cation-disordered high-entropy (CDHE) garnet-type SSE candidates derived from the well-known Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> structure. First, the ML-based surrogate model was incorporated to screen electron-conducting (bandgap (<i>E</i><sub>g</sub>) < 1 eV) and thermodynamically unfavorable (energy above the convex hull (<i>E</i><sub>hull</sub>) > 0.040 eV/atom). Second, the crystal Hamiltonian graph neural network (CHGNet), a recently developed machine learning interatomic potential, was adapted to find the most stable atomic configuration of each composition with the following further DFT relaxations. Third, the CHGNet potential was fine-tuned on the CDHE garnet-type materials. Then, the elastic properties were calculated with retrained CHGNet to confirm whether the candidate materials could suppress dendrite formation and exhibit interfacial stability. Finally, molecular dynamics (MD) simulations equipped with fine-tuned CHGNet potential were conducted to investigate the lithium diffusion characteristics of CDHE garnet-type materials, resulting in the confirmation of three promising CDHE garnet-type candidates with ionic conductivities exceeding 10<sup>-4</sup> S/cm at room temperature. Materials science ; Density functional theory ; Fast ion conductor ; Chemical stability ; Nanotechnology ; Electrolyte ; Chemical physics ; Statistical physics ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion
Authors: Grzegorz Matyszczak | Christopher Jasiak | Gabriela Rusinkiewicz | Kinga Domian | Michał Brzozowski | Krzysztof Krawczyk
Source: Crystals (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Mathematical Modeling of Properties and Structures of Crystals: From Quantum Approach to Machine Learning The crystalline state of matter serves as a reference point in the context of studies of properties of a variety of chemical compounds. This is due to the fact that prepared crystalline solids of practically useful materials (inorganic or organic) may be utilized for the thorough characterization of important properties such as (among others) energy bandgap, light absorption, thermal and electric conductivity, and magnetic properties. For that reason it is important to develop mathematical descriptions (models) of properties and structures of crystals. They may be used for the interpretation of experimental data and, as well, for predictions of properties of novel, unknown compounds (i.e., the design of novel compounds for practical applications such as photovoltaics, catalysis, electronic devices, etc.). The aim of this article is to review the most important mathematical models of crystal structures and properties that vary, among others, from quantum models (e.g., density functional theory, DFT), through models of discrete mathematics (e.g., cellular automata, CA), to machine learning (e.g., artificial neural networks, ANNs). Quantum ; Computer science ; Quantum machine learning ; Theoretical physics ; Artificial intelligence ; Biological system ; Materials science ; Physics ; Quantum mechanics ; Biology ; Quantum computer Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Cihat Güleryüz | Sajjad Hussain Sumrra | Abrar U. Hassan | Ayesha Mohyuddin | Mohamed Mahmoud
Source: Materials Today Communications (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials Materials science ; Degree (music) ; Range (aeronautics) ; Order (exchange) ; Short range order ; Nanotechnology ; Polymer science ; Engineering physics ; Condensed matter physics ; Composite material ; Engineering ; Business Machine Learning in Materials Science ; Polymer crystallization and properties ; Fuel Cells and Related Materials
Authors: Zhaochen Xi | Xin Wang | Chang-Hao Wang | Wei Wang | Jian Bao | Diming Xu | Guoqiang He | Tao Zhou | Guohua Chen | Song Xia | Di Zhou
Source: ACS Applied Materials & Interfaces (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Accelerating Low-<i>k</i> Dielectric Material Discovery: From Graph Machine Learning to Synthesis The exploration of dielectric materials with a specific permittivity remains a significant challenge. Dielectric materials with low permittivity are widely used in semiconductor interlayers and communication substrates for reducing parasitic capacitance and latency transmission. However, discovering novel dielectric materials often relies on a trial-and-error strategy, which is inefficient and time-consuming. Herein, a graph neural network (Res-GCN) is proposed to directly predict permittivity from the connections of atoms, after which a material searching pipeline is developed based on pattern recognition approaches. The model improves relative accuracy by ∼267% over the classical Clausius-Mossotti model, ∼38% over deep neural networks, and ∼17% over crystal convolution neural networks and achieves a root-mean-squared error of ∼1.788 over different crystal symmetries. The high-throughput screening is conducted over 6000 material entries within minutes, identifying promising low-permittivity candidates, and two novel dielectric ceramics with expected properties were discovered and synthesized through only eight targeted experiments. Such machine learning approaches provide an efficient and scalable framework for accelerating the discovery of dielectric materials, significantly shortening the research cycle, and enhancing the synergy between prediction and experiment. Materials science ; Dielectric ; Permittivity ; Artificial neural network ; Scalability ; Computer science ; Artificial intelligence ; Machine learning ; Optoelectronics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Ferroelectric and Piezoelectric Materials
Authors: Bhupalee Kalita | R.I. Zubatyuk | Dylan M. Anstine | Maike Bergeler | Volker Settels | Conrad Stork | Sebastian Spicher | Olexandr Isayev
Source: ChemRxiv (preprint, 2025-08-03)
Field: Materials Science — Materials Chemistry

AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry Open-shell systems such as radical intermediates are central to radical polymerization, combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges. Most of the current machine learning interatomic potentials do not distinguish between different spin states, making them unsuitable for open-shell reactive chemistry. Here we present AIMNet2-NSE (Neural Spin-charge Equilibration), a neural network potential that incorporates spin-charge equilibration for accurate treatment of molecules and reactions with arbitrary charge and spin multiplicities. Built upon the AIMNet2 framework, AIMNet2-NSE is trained on an extensive dataset comprising 20 million closed-shell neutral and charged molecules, 13 million open-shell radical configurations, and 200,000 radical reaction profiles. With explicit handling of spin charges, AIMNet2-NSE enables prediction of spin-resolved properties with near-DFT accuracy while offering five orders of magnitude computational speedup. The predictive capabilities and generalizability of our model are confirmed by evaluations on large-scale radical test sets, the industrially relevant BASChem19 benchmark, and radical polymerization reactions. Overall, AIMNet2-NSE represents a significant advancement in machine learned interatomic potentials, allowing efficient exploration of complex open-shell systems, and significantly advancing our ability to model radical reaction pathways and reactive intermediates in chemical processes where traditional quantum mechanical methods are computationally prohibitive. Chemistry ; Shell (structure) ; Artificial neural network ; Nanotechnology ; Computer science ; Artificial intelligence ; Engineering ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Free Radicals and Antioxidants
Authors: Tomoya Shiota | Kenji Ishihara | Wataru Mizukami
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Lowering the exponential wall: accelerating high-entropy alloy catalysts screening using local surface energy descriptors from neural network potentials High-entropy alloy catalysts are modeled from monometallic data alone through atomic energies of a universal neural network potential, enabling rapid catalyst screening across extensive chemical composition spaces. Artificial neural network ; Alloy ; Exponential function ; Entropy (arrow of time) ; Catalysis ; Materials science ; Statistical physics ; Thermodynamics ; Computer science ; Physics ; Mathematics ; Artificial intelligence ; Chemistry ; Mathematical analysis ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Duo Wang | Ao Xie | Shengcun Ma | Wei Tong | Shiqiang Zou | Anubhav Jain
Source: AI for Science (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Computational catalysis and machine learning applications to water treatment technologies Electrocatalysis presents novel pathways for advanced water treatment. For example, electrocatalytic reduction is an emerging technology for treating oxyanions of concern in water. However, the identification of highly performant, cost-effective catalysts remains a major barrier to deployment at scale. This article discusses how computational modeling and machine learning (ML) can accelerate the search for new catalyst materials for electrocatalytic water treatment processes, such as the electroreduction of oxyanions and degradation of persistent organics. It describes how traditional computational chemistry workflows, now deployed in their basic form for at least two decades, can be expanded in breadth and depth through newly developed machine-learned force fields that have been trained on millions of data points. It also discusses ways in which the theory and ML pipeline can be effectively integrated with experimental synthesis and characterization platforms to rapidly identify and validate new catalyst chemistries for water purification challenges. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and ELM ; Catalytic Processes in Materials Science
Authors: Guoyao Chen | Xuanyu Liu | Yue Zhang | D. Y. Lin | Pingli Mao
Source: Solid State Communications (article, 2025-04-17)
Field: Materials Science — Biomaterials

Leveraging molecular dynamics and machine learning to predict impact performance in polycrystalline magnesium alloys Magnesium ; Crystallite ; Molecular dynamics ; Dynamics (music) ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Chemistry ; Psychology ; Computational chemistry Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Boron and Carbon Nanomaterials Research