jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pablo A. M. Casares | Jack S. Baker | Matija Medvidović | Roberto dos Reis | Juan Miguel Arrazola
Source: The Journal of Chemical Physics (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

GradDFT. A software library for machine learning enhanced density functional theory Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange-correlation energy functionals. GradDFT employs a pioneering parametrization of exchange-correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. Density functional theory ; Computer science ; Scalability ; Differentiable function ; Artificial intelligence ; Machine learning ; Software ; Artificial neural network ; Benchmarking ; Parametrization (atmospheric modeling) ; Theoretical computer science ; Computational chemistry ; Chemistry ; Quantum mechanics ; Mathematics ; Physics ; Programming language ; Database Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Carlos G. Albà | Ismail I.I. Alkhatib | Lourdes F. Vega | Fèlix Llovell
Source: ACS Sustainable Chemistry & Engineering (article, 2024-07-23)
Field: Chemistry — Organic Chemistry

Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors As the EU's mandates to phase out high-GWP refrigerants come into effect, the refrigeration industry is facing a new, unexpected reality: the introduction of more flammable yet environmentally compliant alternatives. This paradigm shift amplifies the need for a rapid, reliable screening methodology to assess the propensity for flammability of emerging fourth generation blends, offering a pragmatic alternative to laborious and time-intensive traditional experimental assessments. In this study, an artificial neural network (ANN) is meticulously constructed, evaluated, and validated to address this emerging challenge by predicting the normalized flammability index (NFI) for an extensive array of pure, binary, and ternary mixtures, reflecting a substantial diversity of compounds like CO<sub>2</sub>, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), six saturated hydrocarbons (sHCs), hydroolefins (HOs), and others. The optimal configuration ([61 (I) × 14 (HL1) × 24 (HL2) × 1 (O)]) demonstrated a profound fit to the data, with metrics like <i>R</i> <sup>2</sup> of 0.999, root-mean-square error (RMSE) of 0.1735, average absolute relative deviation (AARD)% of 0.8091, and SD<sub>av</sub> of ±0.0434. Exhaustive assessments were conducted to ensure the most efficient architecture without compromising the accuracy. Additionally, the analysis of the standardized residuals (SDR) and applicability domain (AD) exhibited fine control and consistency over the data points. External validation using quaternary mixtures further attested to the model's adaptability and predictive capability. The exploration into the relative contribution of descriptors led to the identification of 23 significant sigma descriptors derived from conductor-like screening model (COSMO), responsible for 90.98% of the total contribution, revealing potential avenues for model simplification without a substantial loss in predictive power. Moreover, the model successfully predicted the behavior of prospective industry-relevant mixtures, reinforcing its reliability and opening the door to experimentation with untested blends. The results collectively manifest the developed ANN's efficiency, robustness, and adaptability in modeling flammability, catering to the demands of industry standards, environmental concerns, and safety requirements. Flammability ; Artificial neural network ; Refrigerant ; Space (punctuation) ; Artificial intelligence ; Computer science ; Materials science ; Environmental science ; Process engineering ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Mechanical engineering Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Sensor Technologies ; Thermal and Kinetic Analysis
Authors: Andrea Grisafi | Mathieu Salanne
Source: The Journal of Chemical Physics (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learning method that specifically targets the electronic charge density, also known as SALTED, can be used to predict the long-range response of metal electrodes in model electrochemical cells. In this work, we provide a full integration of SALTED with MetalWalls, a program for performing classical simulations of electrochemical systems. We do so by deriving a spherical harmonics extension of the Ewald summation method, which allows us to efficiently compute the electric field originated by the predicted electrode charge distribution. We show how to use this method to drive the molecular dynamics of an aqueous electrolyte solution under the quantum electric field of a gold electrode, which is matched to the accuracy of density-functional theory. Notably, we find that the resulting atomic forces present a small error of the order of 1 meV/Å, demonstrating the great effectiveness of adopting an electron-density path in predicting the electrostatics of the system. Upon running the data-driven dynamics over about 3 ns, we observe qualitative differences in the interfacial distribution of the electrolyte with respect to the results of a classical simulation. By greatly accelerating quantum-mechanics/molecular-mechanics approaches applied to electrochemical systems, our method opens the door to nanosecond timescales in the accurate atomistic description of the electrical double layer. QM/MM ; Charge (physics) ; Materials science ; Electrochemistry ; Chemical physics ; Computer science ; Nanotechnology ; Physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Electrode Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Anodic Oxide Films and Nanostructures
Authors: Ryotaro Okabe | Abhijatmedhi Chotrattanapituk | Artittaya Boonkird | Nina Andrejevic | Xiang Fu | Tommi Jaakkola | Qichen Song | Thanh Bình Nguyễn | Nathan C. Drucker | Sai Mu | Yao Wang | Bolin Liao | Yongqiang Cheng | Mingda Li
Source: Nature Computational Science (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Virtual node graph neural network for full phonon prediction Computer science ; Node (physics) ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Semiconductor materials and interfaces
Authors: An Chen | Zhilong Wang | Karl Luigi Loza Vidaurre | Yanqiang Han | Simin Ye | Kehao Tao | Shiwei Wang | Jing Gao | Jinjin Li
Source: Journal of Energy Chemistry (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Knowledge-reused transfer learning for molecular and materials science Knowledge transfer ; Computer science ; Knowledge management ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Zhitao Feng | Wentao Guo | Wang‐Yeuk Kong | Dongjie Chen | Shunyang Wang | Dean J. Tantillo
Source: Nature Chemistry (article, 2024-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

Analogies between photochemical reactions and ground-state post-transition-state bifurcations shed light on dynamical origins of selectivity Chemistry ; Selectivity ; Ground state ; Transition state ; Excited state ; Photochemistry ; Computational chemistry ; Adiabatic process ; Kinetic energy ; Chemical physics ; Thermodynamics ; Atomic physics ; Organic chemistry ; Catalysis ; Quantum mechanics ; Physics Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Spectroscopy and Quantum Chemical Studies
Authors: Guangfeng You | Chao Qian | Shurun Tan | Er‐Ping Li | Hongsheng Chen
Source: Physical Review Applied (article, 2024-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Driving deep-learning-based metasurface design with Kramers-Kronig relations Deep-learning-enabled design is an essential part of common metamaterial applications such as diverse metasurfaces, plasmonic nanostructures, and photonic crystals. Further progress in this field has become stultified, though, due to the lack of physical insight in ``black box'' design approaches. The authors propose a neural-network framework that includes an adversary channel based on the familiar Kramers-Kronig relations, yielding high-precision output that conforms to the internal physics even in cases of incomplete physical representations and equations. This framework is also widely applicable, beyond just metasurface design. Kramers–Kronig relations ; Physics ; Computer science ; Optics ; Refractive index Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Millimeter-Wave Propagation and Modeling
Authors: Zhen Liang | Zhimei Liu | Lian Yu | Wenjuan Wang
Source: Process Safety and Environmental Protection (article, 2024-11-02)
Field: Chemistry — Inorganic Chemistry

Fly ash-based zeolites: from waste to value – A comprehensive overview of synthesis, properties, and applications Fly ash ; Waste management ; Value (mathematics) ; Zeolite ; Engineering ; Environmental science ; Chemistry ; Computer science ; Catalysis ; Organic chemistry Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Raquel López-Ríos de Castro | Robert M. Ziolek | Martin B. Ulmschneider | Christian D. Lorenz
Source: Nano Letters (article, 2024-02-02)
Field: Materials Science — Biomaterials

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG–PLGA Polymer Nanoparticles Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-<i>co</i>-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications. PEG ratio ; Nanoparticle ; PLGA ; Polymer ; Chemistry ; Solubilization ; Biophysics ; Chemical engineering ; Nanotechnology ; Materials science ; Biochemistry ; Organic chemistry Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Polydiacetylene-based materials and applications
Authors: Thomas A. Teklemariam | Faith Chou | P. Kumaravel | Jeremy Van Buskrik
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-07-03)
Field: Chemistry — Inorganic Chemistry

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners Adulterant ; Chemistry ; Artificial intelligence ; Principal component analysis ; Chemometrics ; Sugar ; Support vector machine ; Linear discriminant analysis ; Pattern recognition (psychology) ; Cocos nucifera ; Fourier transform infrared spectroscopy ; Partial least squares regression ; Biological system ; Machine learning ; Chromatography ; Food science ; Computer science ; Botany Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Patricia Poths | King C. Lai | Francesco Cannizzaro | Christoph Scheurer | Sebastian Matera | Karsten Reuter
Source: ACS Catalysis (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales We combine automatic process exploration with an iteratively trained machine-learning interatomic potential to systematically identify elementary processes occurring during the initial oxidation of a Pd step edge. Corresponding process lists are a prerequisite to overcome prevalent predictive-quality microkinetic modeling approaches which consider only a minimum number of hand-selected and thus typically intuitive processes. The exploration readily generates close to 3000 inequivalent elementary processes and thus unveils a complexity far beyond current microkinetic modeling capabilities. Among these processes are numerous low-barrier processes involving the collective motion of several atoms that enable a facile O-mediated restructuring of the Pd step edge through the motion of larger PdxOy units. The concomitant interconversion happens on time scales comparable to those of molecular processes of heterogeneous oxidation catalysis. This suggests a dynamic aspect of the operando evolution of the working interface reminiscent of the fluxionality discussed in nanocluster catalysis. Catalysis ; Restructuring ; Process (computing) ; One-Step ; Enhanced Data Rates for GSM Evolution ; Two step ; Process engineering ; Materials science ; Computer science ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Business ; Organic chemistry ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Vijay Choyal | N. Reddy Kala Sagar | Gopalakrishnan Sai Gautam
Source: Journal of Chemical Theory and Computation (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO<sub>2</sub> and TMO<sub>2</sub> compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO<sub>2</sub> frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys. Computer science ; Interatomic potential ; Data science ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Titanium Alloys Microstructure and Properties
Authors: Yijing Tang | Junyu Wei | Yonglan Liu | Yung Chang | Jie Zheng
Source: Langmuir (review, 2024-10-16)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Aided Design and Optimization of Antifouling Surfaces Antifouling surfaces, renowned for their strong surface resistance to proteins, cells, or tissues in various biological and environmental conditions, have broad applications in implanted devices, antibacterial coatings, biosensors, responsive materials, water treatment, and lab-on-a-chip. While extensive experimental research exists on antifouling surfaces, machine learning studies on this topic are relatively few. This perspective specifically focuses on exploring the complex relationships between the composition, structure, and properties of antifouling surfaces, examining how these factors correlate with surface hydration and protein adsorption. Different machine learning models have been developed to analyze and predict single and multiple protein adsorptions on various types of surfaces, ranging from structureless surfaces to well-ordered and rigid self-assembled monolayers, dynamically ordered polymer brushes, and complex filtration membranes. These models not only identify key descriptors or functional groups critical for antifouling performance (surface hydration, protein adsorption) but also predict the antifouling properties for a specific surface. Recognizing current challenges, this perspective delineates future research directions in the antifouling field. By leveraging and comparing current machine learning approaches, it aims to advance both the design and fundamental understanding of antifouling surfaces, thereby pushing the boundaries of innovation in this critical field. Biofouling ; Computer science ; Nanotechnology ; Biochemical engineering ; Materials science ; Chemistry ; Engineering Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Surface Modification and Superhydrophobicity
Authors: Yuchen Chu | Zihao Wang | Wenjun Wang | Yuxi Zeng | Siying He | Yan Chen | Fanzhi Qin | Maihang Wu | Guangming Zeng | Chengyun Zhou
Source: Separation and Purification Technology (review, 2024-08-24)
Field: Materials Science — Materials Chemistry

A review on the algae-derived biochar catalysts:Advanced oxidation processes and machine learning tools Biochar ; Algae ; Catalysis ; Chemical engineering ; Biochemical engineering ; Environmental science ; Chemistry ; Process engineering ; Engineering ; Biology ; Botany ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Adsorption and biosorption for pollutant removal
Authors: Rongkun Chen | Yu Tian | Jiayi Cao | Weina Ren | Shiqian Hu | Chunhua Zeng
Source: Journal of Applied Physics (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures In this research, we utilized density functional theory (DFT) computations to perform ab initio molecular dynamics simulations and static calculations on graphene, hexagonal boron nitride, and their heterostructures, subjecting them to strains, perturbations, twist angles, and defects. The gathered energy, force, and virial information informed the creation of a training set comprising 1253 structures. Employing the Neural Evolutionary Potential framework integrated into Graphics Processing Units Molecular Dynamics, we fitted a machine learning potential (MLP) that closely mirrored the DFT potential energy surface. Rigorous validation of lattice constants and phonon dispersion relations confirmed the precision and dependability of the MLP, establishing a solid foundation for subsequent thermal transport investigations. A further analysis of the impact of twist angles uncovered a significant reduction in thermal conductivity, particularly notable in heterostructures with a decline exceeding 35%. The reduction in thermal conductivity primarily stems from the twist angle-induced softening of phonon modes and the accompanying increase in phonon scattering rates, which intensifies anharmonic interactions among phonons. Our study underscores the efficacy of the MLP in delineating the thermal transport attributes of two-dimensional materials and their heterostructures, while also elucidating the micro-mechanisms behind the influence of the twist angle on thermal conductivity, offering fresh perspectives for the design of advanced thermal management materials. Phonon ; Thermal conductivity ; Graphene ; Materials science ; Heterojunction ; Phonon scattering ; Condensed matter physics ; Ab initio ; Bilayer graphene ; Molecular dynamics ; Density functional theory ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Optoelectronics ; Physics ; Composite material ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Qi Jiang | Zepeng Jia | Sen Lu | Pei Song | Zhikai Gao | Zhiguo Wang | Tiren Peng | Xue Bai | Hong Cui | Weizhi Tian | Rong Feng | Qin Kang | Zhiyong Liang | Hongkuan Yuan
Source: Ceramics International (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Novel NLi4-BGra/MgH2-based heterojunctions for efficient hydrogen storage and modulation of hydrogen-desorption temperature ranges Hydrogen storage ; Materials science ; Desorption ; Magnesium hydride ; Hydrogen ; Density functional theory ; Heterojunction ; Analytical Chemistry (journal) ; Physical chemistry ; Adsorption ; Alloy ; Computational chemistry ; Chemistry ; Metallurgy ; Optoelectronics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems