jablonkagroup/rise_ai_scientists huggingface dataset:
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2020-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

2020年度秋季大会プログラム Synthesis of La-Zn doped SrM ferrite under high oxygen pressureGrinding behavior of Sm2Fe17 alloy powder by surfactant assisted planetary ball milling ○ 福井 章人 1 、カルベート エマ 2 、下島 康嗣 2 、細川 裕之 2 、高木 誠 1 (1.愛知工業大学、2.産業技術総合研究所) 放電プラズマ焼結法により作製した Sm-Fe-N磁石の磁気特性 Magnetic properties of Sm-Fe-N magnets prepared by spark plasma sintering *齋藤 哲治 1 (1.千葉工業大学) 機械学習による Sm-Fe-N系合金粉末の窒素量・酸素量予測 Machine learning prediction for nitrogen and oxygen contents of Sm-Fe-N alloy *細川 裕之 1 、下島 康嗣 1 、 CALVERT Emma 1 、福井 章人 2 (1.国立研究開発法人産業技術総合研究所、2. 愛知工 業大学) α-Fe/Co 0.5 Fe 2.5 O 4 混合粉末-樹脂複合体における電磁波吸収特性 Electromagnetic wave absorption properties of resin composites using α-Fe/Co 0.5 Fe 2.5 O 4 mixed powders ○ 麻 博隆 1 、阿加 賽見 1 、佐藤 光晴 1 、松浦 昌志 1 、手束 展規 1 、杉本 諭 1 (1.東北大学) Fe系粉末-樹脂複合体の高周波特性と電磁ノイズ抑制効果 High frequency properties and noise suppression performance of composite sheet of iron-based powder *佐藤 光晴 1 、豊田 雄一朗 1 、阿加 賽見 1 、松浦 昌志 1 、手束 展規 1 、杉本 諭 1 (1.東北大学) 純鉄圧粉磁心の保磁力に及ぼす密度の影響 Influence of core density on coercive force of iron powder core *高下 拓也 1 、尾﨑 由紀子 2 (1.JFEスチール株式会社、2.九州大学) MIMによる高硬度 Fe-Cr系軟磁性材料の磁気特性 Magnetic Properties of High Hardness Fe-Cr Soft Magnetic Alloys by MIM Process *木村 正宏 1 、清水 透 2 、渡利 久規 2 (1.日本ピストンリング株式会社、2.東京電機大学) Mg-Nb添加窒化アルミニウム/コバルトフェライトマルチフェロイック積層膜の磁気-電気効果 Magneto-electric effect of Mg and Nb added aluminum nitride/cobalt ferrite multiferroic multilayered films.*佐藤 博紀 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) 配向制御用下地膜を用いたコバルトフェライト薄膜の垂直磁気異方性 Perpendicular magnetic anisotropy of the Cobalt ferrite thin films deposited on seed layers *岩動 大樹 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) FeCo系合金-(C 4 F 8 ) n グラニュラー薄膜の軟磁気特性 Soft magnetic properties of FeCo based alloy -(C 4 F 8 ) n granular thin films *郝 泉貴 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) 銅イオンとの相互作用によるフミン酸修飾酸化鉄磁性ナノ粒子の交流磁化特性への影響 Influence on alternating magnetization of humic-acid modified iron-oxide nanoparticles by interaction with Cu ions 原田 有紀 1 、*北本 仁孝 1 (1.東京工業大学) 〇印のついた発表者は優秀講演発表賞の対象者です。 9:00～19:00 *松本 伸彦 1 、三宅 賢武 1 、近藤 幹夫 2 、大石 雄介 3 、外山 和宏 3 (1.(株)豊田中央研究所、2.元 (株)豊田中央研 電動パーキングブレーキ(EPB)用焼結含油軸受の開発 Development of Sintered Oil-impregnated Bearings for Electric Parking Brake(EPB) *久井田 将一 1 (1.(株)ダイヤメット) 広範囲の切削条件に有効な快削性プレミックス鉄粉 Premixed Iron Powder with Machinability Improvement Effect in Wide Range of Cutting Condition *松岡 良輔 1 (1.JFEスチール(株)) 鋭敏化対策ニオブ添加ステンレス材の開発 Development of Niobium-added Stainless steel Sintered material for Desensitization *野田 宗巨 1 (1.住友電気工業(株)) 産業用人協働ロボット用部品の MIM化によるコストダウン Cost-down by Using MIM(Metal Injection Molding) for Industrial Collaborative Robots *野田 豊嗣 1 (1.(株)ファインシンター) Philosophy Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic Properties and Synthesis of Ferrites
Authors: Bo Chen | Qiyu Zeng | Han Wang | Dongdong Kang | Jiayu Dai | A. Ng
Source: arXiv (Cornell University) (preprint, 2020-06-23)
Field: Materials Science — Materials Chemistry

Competitive Effect of Disorder and Defects on Dynamic Structural Transformation of Compressed Gold The microscopic mechanism of dynamic phase transition of noble metals is much demanding and challenging with the development of in situ x-ray diffraction measurements [Phys. Rev. Lett. 124, 235701 (2020); Phys. Rev. Lett. 123, 045702 (2019); Phys. Rev. Lett. 123, 045701 (2019)]. Through rapid compression like shock impact, gold exhibits unique structural transformation characteristics, in which the previous theoretical results are far away from the experiments. In order to simulate the complex process, we construct an interaction potential by training a deep neural network with a set of ab initio calculations. By using molecular dynamics simulations, the structural transformation processes for face-centered cubic (FCC) gold under shock compressions have been unambiguously revealed from the atomic level. In agreement with recent experiments, the sequence of structures, stacking faults, body-centered cubic (BCC) phase, and coexistence of BCC-amorphous phase are found until gold reaching completely melting up to 325 GPa, and a much lower transition pressure (113 GPa) to BCC phase is confirmed. Different from perfect BCC crystalline, the shock compression generates a BCC structure with many disorders, reducing the free energies and consequently largely lowering the transition pressure. Interestingly, by introducing defects in the initial FCC gold, it presents a reverse effect on the FCC-BCC phase transition. These defects in the form of collections of atoms give rise to the formation of the interface, leading to increasing interface barrier and reducing internal stress. Therefore, a competitive effect is highlighted between disorders and defects, determining the FCC-BCC phase transition threshold under dynamical compressions, which is absent in conventional understanding. Materials science ; Phase (matter) ; Stacking fault ; Molecular dynamics ; Amorphous solid ; Phase transition ; Stacking ; Condensed matter physics ; Ab initio ; Dislocation ; Chemical physics ; Crystallography ; Chemistry ; Physics ; Computational chemistry ; Composite material Material Dynamics and Properties ; High-pressure geophysics and materials ; Nonlinear Optical Materials Studies
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: ChemRxiv (preprint, 2020-06-18)
Field: Materials Science — Materials Chemistry

Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density Density Functional Theory (DFT) is the standard formalism to study the electronic structureof matter at the atomic scale. In Kohn-Sham DFT simulations, the balance between accuracyand computational cost depends on the choice of exchange and correlation functional, which onlyexists in approximate form. Here we propose a framework to create density functionals usingsupervised machine learning, termed NeuralXC. These machine-learned functionals are designed tolift the accuracy of baseline functionals towards that are provided by more accurate methods whilemaintaining their efficiency. We show that the functionals learn a meaningful representation of thephysical information contained in the training data, making them transferable across systems. ANeuralXC functional optimized for water outperforms other methods characterizing bond breakingand excels when comparing against experimental results. This work demonstrates that NeuralXCis a first step towards the design of a universal, highly accurate functional valid for both moleculesand solids. Formalism (music) ; Density functional theory ; Computer science ; Hybrid functional ; Correlation ; Representation (politics) ; Functional theory ; Artificial intelligence ; Machine learning ; Statistical physics ; Theoretical computer science ; Algorithm ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2020-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

2020年度秋季大会プログラム 29 日(木) 2020年10月27日(火) 講演特集：磁性材料・磁気デバイスにおける微細構造制御と機能発現 第Ⅰ会場 高飽和磁化材料 La-Zn置換 SrMフェライトの高酸素圧下での合成 Synthesis of La-Zn doped SrM ferrite under high oxygen pressure *和氣 剛 1 、田畑 吉計 1 、中村 裕之 1 (1.京都大学) 界面活性剤を用いた遊星型ボールミルによる Sm2Fe17合金粉の粉砕挙動Grinding behavior of Sm2Fe17 alloy powder by surfactant assisted planetary ball milling ○ 福井 章人 1 、カルベート エマ 2 、下島 康嗣 2 、細川 裕之 2 、高木 誠 1 (1.愛知工業大学、2.産業技術総合研究所) 放電プラズマ焼結法により作製した Sm-Fe-N磁石の磁気特性 Magnetic properties of Sm-Fe-N magnets prepared by spark plasma sintering *齋藤 哲治 1 (1.千葉工業大学) 機械学習による Sm-Fe-N系合金粉末の窒素量・酸素量予測 Machine learning prediction for nitrogen and oxygen contents of Sm-Fe-N alloy *細川 裕之 1 、下島 康嗣 1 、 CALVERT Emma 1 、福井 章人 2 (1.国立研究開発法人産業技術総合研究所、2. 愛知工 業大学) α-Fe/Co 0.5 Fe 2.5 O 4 混合粉末-樹脂複合体における電磁波吸収特性 Electromagnetic wave absorption properties of resin composites using α-Fe/Co 0.5 Fe 2.5 O 4 mixed powders ○ 麻 博隆 1 、阿加 賽見 1 、佐藤 光晴 1 、松浦 昌志 1 、手束 展規 1 、杉本 諭 1 (1.東北大学) 純鉄圧粉磁心の保磁力に及ぼす密度の影響 Influence of core density on coercive force of iron powder core *高下 拓也 1 、尾﨑 由紀子 2 (1.JFEスチール株式会社、2.九州大学) MIMによる高硬度 Fe-Cr系軟磁性材料の磁気特性 Magnetic Properties of High Hardness Fe-Cr Soft Magnetic Alloys by MIM Process *木村 正宏 1 、清水 透 2 、渡利 久規 2 (1.日本ピストンリング株式会社、2.東京電機大学) Mg-Nb添加窒化アルミニウム/コバルトフェライトマルチフェロイック積層膜の磁気-電気効果 Magneto-electric effect of Mg and Nb added aluminum nitride/cobalt ferrite multiferroic multilayered films.*佐藤 博紀 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) 配向制御用下地膜を用いたコバルトフェライト薄膜の垂直磁気異方性 Perpendicular magnetic anisotropy of the Cobalt ferrite thin films deposited on seed layers *岩動 大樹 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) FeCo系合金-(C 4 F 8 ) n グラニュラー薄膜の軟磁気特性 Soft magnetic properties of FeCo based alloy -(C 4 F 8 ) n granular thin films *郝 泉貴 1 、神島 謙二 1 、柿崎 浩一 1 (1.埼玉大学) 銅イオンとの相互作用によるフミン酸修飾酸化鉄磁性ナノ粒子の交流磁化特性への影響 Influence on alternating magnetization of humic-acid modified iron-oxide nanoparticles by interaction with Cu ions 原田 有紀 1 、*北本 仁孝 1 (1.東京工業大学) 〇印のついた発表者は優秀講演発表賞の対象者です。 Medicine Magnetic Properties of Alloys ; Magnetic Properties and Synthesis of Ferrites ; Magnetic Properties and Applications
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source:  (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy. Maxima and minima ; Density functional theory ; Ab initio ; Conformational isomerism ; Coupled cluster ; Electronic structure ; Point (geometry) ; Single point ; Cluster (spacecraft) ; Statistical physics ; Physics ; Molecule ; Molecular physics ; Computer science ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Vészi Blanka | Brigitta Nagy | Nagy Zsombor Kristóf
Source:  (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Acetil-szalicilsav porok és tiszta hatóanyagot tartalmazó kapszulák kioldódásának jóslása a szemcseméret-eloszlás ismeretében In pharmaceutical manufacturing, the idea of non-destructive and real-time testing of raw materials and drugs is receiving increasing attention due to the implementation of continuous manufacturing. There is ample evidence in the literature that the particle size of an active ingredient significantly affects its dissolution rate. However, first-principles-based models have been used so far, instead of including robust, data-rich empirical models. Although first-principles models can provide adequate prediction for simple problems, the empirical approach could have several benefits when complex formulations are studied. In my work, I developed an empirical model that aimed to predict the dissolution of acetylsalicylic acid from the particle size distribution (PSD). For this purpose, the PSD of different acetylsalicylic acid powder fractions and mixtures was measured, and the dissolution curve of the powder mixture and capsules was studied. I compared and optimized different methods (Artificial Neural Networks, PLS regression) depending on the bin number and type of the PSD (i.e. number- and volume-based). The results could contribute to establish a good modeling approach when the effect of PSD on the dissolution of more complex formulations (e.g. tablets) needs to be modeled. Kivonat A gyogyszergyartasban egyre nagyobb figyelmet kap az alapanyag es a keszitmenyek roncsolasmentes es valos-idejű vizsgalata a folyamatos technologiak bevezetese miatt. A szakirodalomban szamtalan bizonyitekot talalni arra, hogy a hatoanyag szemcsemerete jelentősen befolyasolja a tablettak kioldodasat. Azonban, ennek modellezesere leginkabb fizikai osszefuggeseket alkalmaztak eddig, robusztus, sok adattal dolgozo empirikus modelleket nem. Bar az előbbiek egyszerű osszefuggesek modellezesere alkalmasak, az utobbi komplex formulak tanulmanyozasa soran jelentős előnyokkel szolgalhat. Munkam soran egy empirikus modellt optimalizaltam, amely acetil-szalicilsav kioldodasat celzott prediktalni a szemcsemeret-eloszlasbol. Ehhez kulonboző szemcsemeretű acetil-szalicilsav porok frakcioinak es a keverekeinek szemcsemeret-eloszlasat mertem, majd felvettem a porkeverek es tiszta hatoanyagot tartalmazo kapszulak kioldodasgorbejet. Ezutan kulonboző modszereket (mesterseges neuralis halozat, reszleges legkisebb negyzetek modszere) hasonlitottam ossze es optimalizaltam a szemcsemeret-eloszlas osztalyszamainak es tipusanak (szam- vagy terfogatalapu) fuggvenyeben. Az eredmenyek jo kiindulopontkent szolgalhatnak a szemcsemeret-eloszlas komplex keszitmenyek (pl. tablettak) kioldodasara gyakorolt hatasanak tanulmanyozasahoz. Dissolution ; Raw material ; Bin ; Particle size ; Mathematics ; Chemistry ; Algorithm ; Organic chemistry ; Physical chemistry Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Protein purification and stability
Authors: Bradly Alicea | Akshara Gopi | Richard Gordon | Thomas Harbich | Jesse Parent | Asmit Kumar Singh | U. Singh
Source: Figshare (article, 2020-01-01)
Field: Materials Science — Biomaterials

The Psychophysics of Non-neuronal Cognition Non-neuronal cognition can be used to explain what appear to be neural behaviors in cellular-level systems. Examples include cellular decision-making (Perkins and Swain, 2009), information processing in regenerative processes (Baluskaand Levin, 2016), and active perception among filopodia (Aspalter et.al, 2020). We characterize movement among colonies of the diatom genus Bacillaria as a collectivepattern generator (CoPG). The CoPG is similar to a central pattern generator (CPG) found in insect nervous systems. Collective pattern generators are produced by thestretching motions produced by the actin filaments of two neighboring cells. These synchronized movements propagate down the colony in an undulating, accordion -likemanner. While we can utilize a neural model to characterize the behavior of this type of multicellular colony, psychophysical models are used to characterize pseudo-neuronal behaviors. We will use a database of images and movies of Bacillaria movement (Alicea et.al, 2019) to capture measurements of colony movement and to train a generativemodel of movement dynamics. Using these data and a model, we can then establish an input/output curve that determines how much input results in a corresponding degree of output. From these input/output curves, we can derive just-noticeable differences and signal detection measurements. To model the movements that lead to statistical regularities, we propose a connectionist model in which nodes (neuronal units) can act as neuronal oscillators. Collectively, these nodes form the components of a CoPG.Meanwhile, connectivity is determined by the relative degree of synchronization between neighboring cells.<br>ReferencesAlicea, B., Gordon, R., Harbich, T., Singh, U., Singh, A., and Varma, V. (2019). Towards a Digital Diatom: image processing and deep learning analysis of Bacillariaparadoxa dynamic morphology. <i>bioRxiv</i>, doi:10.1101/ 2019.12.21.885897.<br>Aspalter, I.M. et.al (2020). Active Perception during Angiogenesis: filopodia speed up Notch selection of tip cells in silico and in vivo. <i>bioRxiv</i>, doi:10.1101/2020.08. 22.261263.<br>Baluska, F. and Levin, M. (2016). On Having No Head: cognition throughout Biological Systems. <i>Frontiers in Psychology</i>, 7, 902.<br>Perkins, T.J. and Swain, P.S. (2009). Strategies for cellular decision-making. <i>Molecular Systems Biology</i>, 5, 326. Central pattern generator ; Connectionism ; Neuroscience ; Computer science ; Movement (music) ; Perception ; Filopodia ; SIGNAL (programming language) ; Psychophysics ; Artificial intelligence ; Biological system ; Communication ; Artificial neural network ; Biology ; Physics ; Psychology ; Rhythm Diatoms and Algae Research
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-04-13)
Field: Materials Science — Materials Chemistry

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %Ecorr. None of the DFT-based diagnostics are nearly as predictive of %Ecorr as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening. Pairwise comparison ; Computer science ; Machine learning ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Olivier Charles Gagné
Source: ChemRxiv (preprint, 2020-11-01)
Field: Chemistry — Inorganic Chemistry

On the Crystal Chemistry of Inorganic Nitrides: Crystal-Chemical Parameters, Bonding Behavior, and Opportunities in the Exploration of Their Compositional Space The scarcity of nitrogen in Earth’s crust, combined with challenging synthesis, have made inorganic nitrides a relatively unexplored class of compounds compared to their naturally-abundant oxide counterparts. To facilitate exploration of their compositional space via a priori modeling, and to help a posteriori structure verification not limited to inferring the oxidation state of redox-active cations, we derive a suite of bond-valence parameters and Lewis-acid strength values for 76 cations observed bonding to N3-, and further outline a baseline statistical knowledge of bond lengths for these compounds. Examination of structural and electronic effects responsible for the functional properties and anomalous bonding behavior of inorganic nitrides shows that many mechanisms of bond-length variation ubiquitous to oxide and oxysalt compounds (e.g., lone-pair stereoactivity, the Jahn-Teller and pseudo Jahn-Teller effects) are similarly pervasive in inorganic nitrides, and are occasionally observed to result in greater distortion magnitude than their oxide counterparts. We identify promising functional units for exploring uncharted chemical spaces of inorganic nitrides, e.g. multiple-bond metal centers with promise regarding the development of a post-Haber-Bosch process proceeding at milder reaction conditions, and promote an atomistic understanding of chemical bonding in nitrides relevant to such pursuits as the development of a model of ion substitution in solids, a problem of great relevance to semiconductor doping whose resolve would fast-track the development of compound solar cells, battery materials, electronics, and more. Nitride ; Chemical bond ; Valence (chemistry) ; Oxide ; Crystal chemistry ; Chemistry ; Materials science ; Chemical physics ; Crystal structure ; Inorganic chemistry ; Nanotechnology ; Computational chemistry ; Crystallography ; Metallurgy Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Crystal Structures and Properties
Source: Angewandte Chemie International Edition (article, 2020-11-16)
Field: Chemistry — Organic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 48/2020 ElectrocatalysisAp orous 3D superstructure of carbon nanosheets with ultrafine cobalt phosphide nanoparticles was synthesized by Q. Xu et al. in their Communication on page 21360.Ther esultant CoP x @CNS shows high activity in water splitting. Citation ; INT ; Computer science ; Library science ; Information retrieval ; Programming language Click Chemistry and Applications ; Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods
Authors: Sarah I. Allec
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Atomistic Modeling of Amorphous Materials Amorphous (non-crystalline) materials offer new capabilities and applications relative to their corresponding crystalline phases due to their novel and tunable material properties, including but not limited to larger surface area, wider substrate compatibility, stronger corrosion resistance, and lower temperature processing than their crystal counterparts. However, even the most advanced experimental techniques (e.g., in situ NMR and ``inverse" approaches such as Reverse Monte Carlo (RMC) modeling of diffraction data) cannot directly measure the bulk atomic structure of such complex, non-crystalline materials. This, coupled with the lack of a structural theory of amorphous materials, has limited our understanding about the structure-property relationships required for the optimization of realistic devices. In this thesis, we compare and combine several atomistic modeling techniques for generating accurate and general structures of amorphous systems, both solid and liquid. We develop several metrics for measuring efficiency of configuration space sampling to guide and standardize the computational study of amorphous materials, and use these to differentiate between different samples of amorphous structures. Lastly, we use these structures to help elucidate structure-property relationships in these systems. In amorphous metal oxides, we characterize the types of coordination defects in amorphous metal oxide systems and subsequently identify potential electron and hole traps in their electronic structures. In a liquid system, water-in-salt electrolytes, we evaluate how the hydrogen bonding network and ion coordination shells change with composition, and how these are directly correlated to battery performance. Amorphous solid ; Materials science ; Reverse Monte Carlo ; Amorphous metal ; Nanotechnology ; Chemical physics ; Crystal structure ; Crystallography ; Chemistry ; Neutron diffraction Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis