jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raúl P. Peláez | Guillem Simeon | Raimondas Galvelis | Antonio Mirarchi | Peter Eastman | Stefan Doerr | Philipp Thölke | Thomas E. Markland | Gianni De Fabritiis
Source: Journal of Chemical Theory and Computation (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net. Computer science ; Modular design ; Software ; Computation ; Enhanced Data Rates for GSM Evolution ; Acceleration ; Artificial neural network ; Transformation (genetics) ; Ranging ; Computational science ; Distributed computing ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Nana Cao | Kang Huang | Jianjun Xie | Hui Wang | Xinghua Shi
Source: Nano Today (article, 2024-01-25)
Field: Materials Science — Biomaterials

Self-assembly of peptides: The acceleration by molecular dynamics simulations and machine learning Acceleration ; Molecular dynamics ; Dynamics (music) ; Computer science ; Nanotechnology ; Statistical physics ; Materials science ; Physics ; Chemistry ; Classical mechanics ; Computational chemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Antimicrobial Peptides and Activities
Authors: Simon d’Oelsnitz | Daniel J. Diaz | Wantae Kim | Daniel J. Acosta | Tyler L. Dangerfield | Mason W. Schechter | Matthew B. Minus | James R. Howard | Hannah Do | James M. Loy | Hal S. Alper | Yan Zhang | Andrew D. Ellington
Source: Nature Communications (article, 2024-03-07)
Field: Chemistry — Organic Chemistry

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC<sub>50</sub> = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4'-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations. Amaryllidaceae Alkaloids ; Rational design ; Biosensor ; Enzyme ; Protein engineering ; Computational biology ; Biocatalysis ; Amaryllidaceae ; Biomanufacturing ; Chemistry ; Combinatorial chemistry ; Alkaloid ; Biology ; Biochemistry ; Biotechnology ; Nanotechnology ; Stereochemistry ; Materials science ; Botany Chemical synthesis and alkaloids ; Psychedelics and Drug Studies ; Alkaloids: synthesis and pharmacology
Authors: Seong-Hwan Kim | Jeheon Woo | Woo Youn Kim
Source: Nature Communications (article, 2024-01-06)
Field: Materials Science — Materials Chemistry

Diffusion-based generative AI for exploring transition states from 2D molecular graphs The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration. Computer science ; Generative grammar ; Generative model ; Diffusion ; Transition (genetics) ; Transition state ; Biological system ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Chemistry ; Physics ; Thermodynamics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seong-Min Kim | Yousung Jung | Joshua Schrier
Source: Journal of the American Chemical Society (article, 2024-07-11)
Field: Materials Science — Materials Chemistry

Large Language Models for Inorganic Synthesis Predictions We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists. Chemistry ; Computational chemistry Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Sheng Bi | Lisanne Knijff | Xiliang Lian | Alicia van Hees | Chao Zhang | Mathieu Salanne
Source: ACS Nano (review, 2024-07-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries for high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures and (surface) reactivity. Among them, nanocarbons are the most important for practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, and a wide variety of metal oxides. These heterogeneous and complex electrode materials are difficult to model with conventional approaches. However, the development of computational methods, the incorporation of machine learning techniques, and the increasing power in high performance computing now allow us to tackle these types of systems. In this Review, we summarize the current efforts in this direction. We show that depending on the nature of the materials and of the charging mechanisms, different methods, or combinations of them, can provide desirable atomic-scale insight on the interactions at play. We mainly focus on two important aspects: (i) the study of ion adsorption in complex nanoporous materials, which require the extension of constant potential molecular dynamics to multicomponent systems, and (ii) the characterization of Faradaic processes in pseudocapacitors, that involves the use of electronic structure-based methods. We also discuss how recently developed simulation methods will allow bridges to be made between double-layer capacitors and pseudocapacitors for future high power electricity storage devices. Pseudocapacitor ; Supercapacitor ; Nanotechnology ; Materials science ; Nanoporous ; Characterization (materials science) ; Computer science ; Electrode ; Capacitance ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Ling Yuan | Mujian Xu | Yanyang Zhang | Zhihong Gao | Lingxin Zhang | Chen Cheng | Chenghan Ji | Ming Hua | Lu Lv | Weiming Zhang
Source: Separation and Purification Technology (article, 2024-02-10)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted screening of metal-organic frameworks (MOFs) for the removal of heavy metals in aqueous solution Aqueous solution ; Heavy metals ; Metal-organic framework ; Metal ; Chemistry ; Environmental chemistry ; Chemical engineering ; Organic chemistry ; Adsorption ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Advanced Nanomaterials in Catalysis
Authors: Daniel Koch | Michele Pavanello | Xuecheng Shao | Manabu Ihara | Paul W. Ayers | Chérif F. Matta | Samantha Jenkins | Sergei Manzhos
Source: Chemical Reviews (review, 2024-11-15)
Field: Materials Science — Surfaces, Coatings and Films

The Analysis of Electron Densities: From Basics to Emergent Applications The electron density determines all properties of a system of nuclei and electrons. It is both computable and observable. Its topology allows gaining insight into the mechanisms of bonding and other phenomena in a way that is complementary to and beyond that available from the molecular orbital picture and the formal oxidation state (FOS) formalism. The ability to derive mechanistic insight from electron density is also important with methods where orbitals are not available, such as orbital-free density functional theory (OF-DFT). While density topology-based analyses such as QTAIM (quantum theory of atoms-in-molecules) have been widely used, novel, vector-based techniques recently emerged such as next-generation (NG) QTAIM. Density-dependent quantities are also actively used in machine learning (ML)-based methods, in particular, for ML DFT functional development, including machine-learnt kinetic energy functionals. We review QTAIM and its recent extensions such as NG-QTAIM and localization-delocalization matrices (LDM) and their uses in the analysis of bonding, conformations, mechanisms of redox reactions excitations, as well as ultrafast phenomena. We review recent research showing that direct density analysis can circumvent certain pitfalls of the FOS formalism, in particular in the description of anionic redox, and of the widely used (spherically) projected density of states analysis. We discuss uses of density-based quantities for the construction of DFT functionals and prospects of applications of analyses of density topology to get mechanistic insight with OF-DFT and recently developed time-dependent OF-DFT. Chemistry ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Yuxuan Sun | Peihao Sun | Jixiu Jia | Ziyun Liu | Lili Huo | Lixin Zhao | Yanan Zhao | Wenjuan Niu | Zonglu Yao
Source: Chemical Engineering Journal (article, 2024-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning in clarifying complex relationships: Biochar preparation procedures and capacitance characteristics Biochar ; Supercapacitor ; Capacitance ; Energy storage ; Specific energy ; Materials science ; Process engineering ; Renewable energy ; Biological system ; Nanotechnology ; Electrode ; Chemical engineering ; Chemistry ; Thermodynamics ; Electrical engineering ; Engineering ; Pyrolysis ; Physics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrocatalysts for Energy Conversion
Authors: Selvaraj Kunjiappan | Lokesh Kumar Ramasamy | K. Suthendran | Parasuraman Pavadai | Panneerselvam Theivendren | Ponnusamy Palanisamy
Source: Scientific Reports (article, 2024-01-12)
Field: Chemistry — Analytical Chemistry

Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X<sub>1</sub>: 0.5-1 mm), methanol concentration (X<sub>2</sub>: 60-70% in distilled water), ultrasound exposure time (X<sub>3</sub>: 18-28 min), temperature (X<sub>4</sub>: 35-45 °C), and ultrasound intensity (X<sub>5</sub>: 65-75 W cm<sup>-2</sup>) at five levels (- 2, - 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y<sub>1</sub>; TPC), total flavonoid content (y<sub>2</sub>; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y<sub>3</sub>; %DPPH*sc), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y<sub>4</sub>; %ABTS*sc) and Ferric ion reducing antioxidant potential (y<sub>5</sub>; FRAP) were selected. The optimized condition was observed at X<sub>1</sub> = 0.155 mm, X<sub>2</sub> = 65% methanol in water, X<sub>3</sub> = 23 min ultrasound exposure time, X<sub>4</sub> = 40 °C, and X<sub>5</sub> = 70 W cm<sup>-2</sup> ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract. ABTS ; DPPH ; Extraction (chemistry) ; Response surface methodology ; Polyphenol ; Chemistry ; Adaptive neuro fuzzy inference system ; Distilled water ; Methanol ; Antioxidant ; Chromatography ; Biochemistry ; Organic chemistry ; Artificial intelligence ; Computer science ; Fuzzy logic Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Microbial Inactivation Methods
Authors: Catarina Franco Jones | Leonor Resina | Frederico Castelo Ferreira | Paola Sanjuan‐Alberte | Teresa Esteves
Source: The Journal of Physical Chemistry C (article, 2024-07-01)
Field: Materials Science — Polymers and Plastics

Conductive Core–Shell Nanoparticles: Synthesis and Applications “Smart” materials are gaining widespread attention and popularity due to their innovative ability to provide additional functionalities that go beyond the capabilities of traditional materials. Moreover, interesting phenomena surge at the transition from the microscale to the nanoscale, where significant changes in their properties occur due to the emergence of quantum effects. However, these often combine materials of different nature which might be difficult to merge. This can be solved by the use of core–shell structures, as they have the ability to combine different materials with an enhanced functionality allowing for their application-specific tailoring. Here, various synthesis techniques of core–shell nanostructures featuring a conductive shell are outlined, including physical and chemical methods, and their influence on the final electrical properties of the nanoparticles. This work also discusses the most common conductive polymers used as shell materials, exploring their impact on the nanoparticles’ performance. Applications across a wide range of fields ranging from biomedicine for cancer intervention to energy storage, catalysis, and microelectronics are reviewed, showcasing the potential of these versatile nanoparticles to drive technological advances. Challenges related to their synthesis, material compatibility, and environmental concerns are acknowledged, with suggestions for future research directions. Selecting the appropriate synthesis strategy is pivotal for the successful development of conductive core–shell nanoparticles, directly impacting their functionality and applicability. Nanoparticle ; Electrical conductor ; Materials science ; Core (optical fiber) ; Nanotechnology ; Shell (structure) ; Composite material Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: José M. Castillo-Robles | Ernane de Freitas Martins | Pablo Ordejón | Ivan Cole
Source: npj Materials Degradation (review, 2024-07-12)
Field: Materials Science — Materials Chemistry

Molecular modeling applied to corrosion inhibition: a critical review Abstract In the last few years, organic corrosion inhibitors have been used as a green alternative to toxic inorganic compounds to prevent corrosion in materials. Nonetheless, the fundamental mechanisms determining their inhibition performance are still far from understood. Molecular modeling can provide important insights into those mechanisms, allowing for a detailed analysis of the corrosion inhibition (CI) process. However, CI modeling is frequently underexplored and commonly used in a standardized way following a pre-determined recipe to support experimental data. We highlight six fundamental aspects (A) that one should consider when modeling CI: (A1) the electronic properties of isolated inhibitors, (A2) the interaction of the inhibitor with the surface, (A3) the surface model, (A4) the effect of the anodic and cathodic zones on the surface, (A5) the solvent effects, and (A6) the electrodes’ potential effects. While A1-A3 are more frequently investigated, A4-A6 and some more complex surface models from A3 are usually not considered and represent gaps in the CI modeling literature. In this review, we discuss the main features of molecular modeling applied to CI, considering the aforementioned key aspects and focusing on the gaps that the emerging approaches aim to fill. Filling these gaps will allow performing more detailed simulations of the CI process, which, coupled with artificial intelligence (AI) methods and multiscale approaches, might construct the bridge between the nanoscale CI modeling and the continuum scale of the CI processes. Corrosion ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Phuong V. Pham | The-Hung Mai | Huy‐Binh Do | М. Vasundhara | Van‐Huy Nguyen | Trieu Nguyen | Hao Van Bui | Van‐Duong Dao | Ram K. Gupta | Vinoth Kumar Ponnusamy | Jin‐Hong Park
Source: Chemical Society Reviews (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Layer-by-layer thinning of two-dimensional materials Etching technology - one of the representative modern semiconductor device makers - serves as a broad descriptor for the process of removing material from the surfaces of various materials, whether partially or entirely. Meanwhile, thinning technology represents a novel and highly specialized approach within the realm of etching technology. It indicates the importance of achieving an exceptionally sophisticated and precise removal of material, layer-by-layer, at the nanoscale. Notably, thinning technology has gained substantial momentum, particularly in top-down strategies aimed at pushing the frontiers of nano-worlds. This rapid development in thinning technology has generated substantial interest among researchers from diverse backgrounds, including those in the fields of chemistry, physics, and engineering. Precisely and expertly controlling the layer numbers of 2D materials through the thinning procedure has been considered as a crucial step. This is because the thinning processes lead to variations in the electrical and optical characteristics. In this comprehensive review, the strategies for top-down thinning of representative 2D materials (<i>e.g.</i>, graphene, black phosphorus, MoS<sub>2</sub>, h-BN, WS<sub>2</sub>, MoSe<sub>2</sub>, and WSe<sub>2</sub>) based on conventional plasma-assisted thinning, integrated cyclic plasma-assisted thinning, laser-assisted thinning, metal-assisted splitting, and layer-resolved splitting are covered in detail, along with their mechanisms and benefits. Additionally, this review further explores the latest advancements in terms of the potential advantages of semiconductor devices achieved by top-down 2D material thinning procedures. Thinning ; Layer (electronics) ; Materials science ; Chemistry ; Composite material ; Biology Graphene research and applications ; Photonic Crystals and Applications ; 2D Materials and Applications