DS BIOVIA Materials Studio 2024 v24.1.0.321190

DS BIOVIA Materials Studio 完整版独立离线安装程序。它是一个集成的多尺度建模环境。

Overview of DS BIOVIA Materials Studio
DS BIOVIA Materials Studio 概述

This is a complete modeling and simulation environment designed to allow materials science and chemistry researchers to predict and understand the relationships of a material’s atomic and molecular structure with its properties and behavior.
这是一个完整的建模和仿真环境，旨在让材料科学和化学研究人员能够预测和理解材料的原子和分子结构与其特性和行为之间的关系。

Using this program, researchers in many industries are engineering better-performing materials of all types, including catalysts, polymers, composites, metals, alloys, pharmaceuticals, batteries, and more. It offers an “in silico first” approach, allowing researchers to optimize the performance of their materials in a relatively low-cost environment before physical testing.
使用该程序，许多行业的研究人员正在设计各种类型的性能更好的材料，包括催化剂、聚合物、复合材料、金属、合金、药物、电池等。它提供了一种“计算机优先”的方法，允许研究人员在物理测试之前在相对低成本的环境中优化其材料的性能。

Included: 包括：

Battery Materials 电池材料
Catalyst Design 催化剂设计
Chemicals & Solvents 化学品和溶剂
Consumer Packaged Goods 包装消费品
Materials Science Collection 材料科学馆藏
Metal Alloy Design 金属合金设计
Pharmaceutical Development 药物开发
Photovoltaics & Organic Electronics
光伏和有机电子
Polymer Composites 聚合物复合材料
Semiconductors & Sensors 半导体和传感器
Visualization & Statistics 可视化和统计

Features of BIOVIA Materials Studio
BIOVIA Materials Studio 的特点

Offers an “in silico first” approach, allowing researchers to optimize their materials’ performance in a relatively low-cost environment before physical testing
提供“计算机优先”方法，允许研究人员在物理测试之前在相对低成本的环境中优化其材料的性能
Accelerate the innovation process: Drive a deeper understanding of the interactions that define material properties.
加速创新过程：更深入地了解定义材料属性的相互作用。
Reduce R&D Costs: Minimize the number of physical experiments via “Virtual screening” candidates.
降低研发成本：通过“虚拟筛选”候选者最大限度地减少物理实验的数量。
Improve R&D Efficiency: Automate and share best practices within Pipeline Pilot to reduce non-value-added tasks.
提高研发效率：在Pipeline Pilot中自动化并分享最佳实践，以减少非增值任务。
Foster Data-Driven Decisions: Complement laboratory experimentation with robust materials informatics
促进数据驱动型决策：使用强大的材料信息学补充实验室实验