jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sumner B. Harris | Arpan Biswas | Seok Joon Yun | Kevin M. Roccapriore | Christopher M. Rouleau | Alexander A. Puretzky | Rama K. Vasudevan | David B. Geohegan | Kai Xiao
Source: Small Methods (article, 2024-04-28)
Field: Materials Science — Materials Chemistry

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe<sub>2</sub> films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe<sub>2</sub> films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD. Workflow ; Bayesian optimization ; Automation ; Materials science ; Pulsed laser deposition ; Deposition (geology) ; Characterization (materials science) ; Thin film ; Computer science ; Nanotechnology ; Throughput ; Artificial intelligence ; Engineering ; Mechanical engineering ; Wireless Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Ryan A. Shenvi
Source: ACS Central Science (review, 2024-02-14)
Field: Chemistry — Organic Chemistry

Natural Product Synthesis in the 21st Century: Beyond the Mountain Top Research into natural products emerged from humanity's curiosity about the nature of matter and its role in the <i>materia medica</i> of diverse civilizations. Plants and fungi, in particular, supplied materials that altered behavior, perception, and well-being profoundly. Many active principles remain well-known today: strychnine, morphine, psilocybin, ephedrine. The potential to circumvent the constraints of natural supply and explore the properties of these materials led to the field of natural product synthesis. This research delivered new molecules with new properties, but also led to fundamental insights into the chemistry of the nonmetal elements H, C, N, O, P, S, Se, and their combinations, i.e., organic chemistry. It also led to a potent culture focused on bigger molecules and races to the finish line, perhaps at the expense of actionable next steps. About 20 years ago, the field began to contract in the United States. Research that focused solely on chemical reaction development, especially catalysis, filled the void. After all, new reactions and mechanistic insight could be immediately implemented by the chemistry community, so it became hard to justify the lengthy procurement of a complex molecule that sat in the freezer unused. This shift coincided with a divestment of natural product portfolios by pharmaceutical companies and an emphasis in academic organic chemistry on applications-driven research, perhaps at the expense of more fundamental science. However, as bioassays and the tools of chemical biology become widespread, synthesis finds a new and powerful ally that allows us to better deliver on the premise of the field. And the hard-won insights of complex synthesis can be better encoded digitally, mined by data science, and applied to new challenges, as chemists perturb and even surpass the properties of complex natural products. The 21st century promises powerful developments, both in fundamental organic chemistry and at the interface of synthesis and biology, if the community of scientists fosters its growth. This essay tries to contextualize natural product synthesis for a broad audience, looks ahead to its transformation in the coming years, and expects the future to be bright. Nanotechnology ; Natural product ; Chemistry ; Premise ; Biochemical engineering ; Organic chemistry ; Engineering ; Materials science ; Epistemology ; Philosophy Chemical synthesis and alkaloids ; Microbial Natural Products and Biosynthesis ; Advanced Synthetic Organic Chemistry
Authors: Shizhao Lu | Arthi Jayaraman
Source: Progress in Polymer Science (article, 2024-05-03)
Field: Materials Science — Materials Chemistry

Machine learning for analyses and automation of structural characterization of polymer materials Workflow ; Computer science ; Context (archaeology) ; Characterization (materials science) ; Automation ; Metadata ; Artificial intelligence ; Process (computing) ; Data science ; Machine learning ; Nanotechnology ; Engineering ; World Wide Web ; Materials science ; Mechanical engineering ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Hung Tuan Nguyen | Ryotaro Okabe | Abhijatmedhi Chotrattanapituk | Mingda Li
Source: Advanced Materials (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Universal Ensemble‐Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials and applications. Materials science ; Quantum computer ; Photovoltaics ; Refractive index ; Embedding ; Computer science ; Quantum ; Photovoltaic system ; Optoelectronics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Catalysis and Oxidation Reactions
Authors: Raidel Martin‐Barrios | Edisel Navas‐Conyedo | Xuyi Zhang | Yunwei Chen | Jorge Gulı́n González
Source: International Journal of Quantum Chemistry (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

An overview about neural networks potentials in molecular dynamics simulation Abstract Ab‐initio molecular dynamics (AIMD) is a key method for realistic simulation of complex atomistic systems and processes in nanoscale. In AIMD, finite‐temperature dynamical trajectories are generated by using forces computed from electronic structure calculations. In systems with high numbers of components a typical AIMD run is computationally demanding. On the other hand, machine learning (ML) is a subfield of the artificial intelligence that consist in a set of algorithms that show learning by experience with the use of input and output data where algorithms are capable of analysing and predicting the future. At present, the main application of ML techniques in atomic simulations is the development of new interatomic potentials to correctly describe the potential energy surfaces (PES). This technique is in constant progress since its inception around 30 years ago. The ML potentials combine the advantages of classical and Ab‐initio methods, that is, the efficiency of a simple functional form and the accuracy of first principles calculations. In this article we review the evolution of four generations of machine learning potentials and some of their most notable applications. This review focuses on MLPs based on neural networks. Also, we present a state of art of this topic and future trends. Finally, we report the results of a scientometric study (covering the period 1995–2023) about the impact of ML techniques applied to atomistic simulations, distribution of publications by geographical regions and hot topics investigated in the literature. Computer science ; Molecular dynamics ; Artificial neural network ; Statistical physics ; Ab initio ; Set (abstract data type) ; Artificial intelligence ; Electronic structure ; Machine learning ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yuxiang Wang | Li Yang | Zechen Tang | He Li | Zilong Yuan | Honggeng Tao | Nianlong Zou | Ting Bao | Xinghao Liang | Zezhou Chen | Shanghua Xu | Ce Bian | Zhiming Xu | Chong Wang | Si Chen | Wenhui Duan | Yong Xu
Source: Science Bulletin (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Universal materials model of deep-learning density functional theory Hamiltonian Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH’s universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery. Computer science ; Deep learning ; Artificial intelligence ; Property (philosophy) ; Nanotechnology ; Materials science ; Epistemology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: He Zhang | Siyuan Liu | Jiacheng You | Chang Liu | Shuxin Zheng | Ziheng Lu | Tong Wang | Nanning Zheng | Bin Shao
Source: Nature Computational Science (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning Density functional theory ; Scaling ; Orbital-free density functional theory ; Range (aeronautics) ; Statistical physics ; Representation (politics) ; Atomic orbital ; Quantum chemistry ; Locality ; Basis (linear algebra) ; Quantum ; Molecular orbital ; Computer science ; Deep learning ; Molecule ; Physics ; Chemistry ; Hybrid functional ; Quantum mechanics ; Artificial intelligence ; Materials science ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dong Hyeon Mok | Seoin Back
Source: Journal of the American Chemical Society (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Generative Pretrained Transformer for Heterogeneous Catalysts Discovery of novel and promising materials is a critical challenge in the field of chemistry and material science, traditionally approached through methodologies ranging from trial-and-error to machine-learning-driven inverse design. Recent studies suggest that transformer-based language models can be utilized as material generative models to expand the chemical space and explore materials with desired properties. In this work, we introduce the catalyst generative pretrained transformer (CatGPT), trained to generate string representations of inorganic catalyst structures from a vast chemical space. CatGPT not only demonstrates high performance in generating valid and accurate catalyst structures but also serves as a foundation model for generating the desired types of catalysts by text-conditioning and fine-tuning. As an example, we fine-tuned the pretrained CatGPT using a binary alloy catalyst data set designed for screening two-electron oxygen reduction reaction (2e-ORR) catalyst and generated catalyst structures specialized for 2e-ORR. Our work demonstrates the potential of generative language models as generative tools for catalyst discovery. Generative grammar ; Chemical space ; Transformer ; Catalysis ; Chemistry ; Generative model ; Inverse ; Binary number ; Artificial intelligence ; Computer science ; Drug discovery ; Organic chemistry ; Engineering ; Voltage Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Topic Modeling
Authors: Xiguang Wu | Wenjiang Zhou | Haikuan Dong | Penghua Ying | Yanzhou Wang | Bai Song | Zheyong Fan | Shiyun Xiong
Source: The Journal of Chemical Physics (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors. Thermostat ; Phonon ; Statistical physics ; Thermal conductivity ; Gaussian ; Molecular dynamics ; Langevin dynamics ; Context (archaeology) ; Computer science ; Physics ; Lattice (music) ; Materials science ; Condensed matter physics ; Thermodynamics ; Quantum mechanics ; Acoustics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Nathan J. Szymanski | Sean Fu | Ellen Persson | Gerbrand Ceder
Source: npj Computational Materials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization. Reciprocal lattice ; Representation (politics) ; Diffraction ; Computer science ; Inference ; Artificial intelligence ; Convolutional neural network ; Fourier transform ; Pattern recognition (psychology) ; Phase (matter) ; Algorithm ; Machine learning ; Mathematics ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Kenichiro Takaba | Anika J. Friedman | Chapin E. Cavender | Pavan Kumar Behara | Iván Pulido | Michael M. Henry | Hugo MacDermott-Opeskin | Christopher R. Iacovella | Arnav Nagle | Alexander Matthew Payne | Michael R. Shirts | David L. Mobley | John D. Chodera | Yuanqing Wang
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine-learned molecular mechanics force fields from large-scale quantum chemical data The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. Preprint ; Force field (fiction) ; License ; Field (mathematics) ; Computer science ; Scale (ratio) ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematics ; World Wide Web Machine Learning in Materials Science ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Yongqin Zhao | Jihong Deng | Quansheng Chen | Hui Jiang
Source: Food Chemistry X (article, 2024-03-21)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:math> g <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∙</mml:mo></mml:mrow></mml:math> kg<sup>-1</sup> and a coefficient of determination on the prediction (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math>) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool. Zearalenone ; Convolutional neural network ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Chromatography ; Optics ; Physics ; Mycotoxin ; Food science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Mycotoxins in Agriculture and Food
Authors: Mahmood Yousefi | Hesam Akbari | Amir Adibzadeh | Aliakbar Mohammadi | Mansour Baziar | Mohsen Abbasi Farajzadeh | Hamed Akbari
Source: Chemosphere (article, 2024-01-21)
Field: Chemistry — Inorganic Chemistry

Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models Diazinon ; Adsorption ; Langmuir adsorption model ; Box–Behnken design ; Aqueous solution ; Response surface methodology ; Graphene ; Chemistry ; Chemical engineering ; Materials science ; Nuclear chemistry ; Chromatography ; Pesticide ; Nanotechnology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications